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Sariturk, Batuhan, and Dursun Zafer Seker. "A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images." Sensors 22, no. 19 (October 8, 2022): 7624. http://dx.doi.org/10.3390/s22197624.
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Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs’ inability to model global context and Transformers’ high memory need. In this study, 10 CNN and Transformer models were generated, and comparisons were realized. Alongside our proposed Residual-Inception U-Net (RIU-Net), U-Net, Residual U-Net, and Attention Residual U-Net, four CNN architectures (Inception, Inception-ResNet, Xception, and MobileNet) were implemented as encoders to U-Net-based models. Lastly, two Transformer-based approaches (Trans U-Net and Swin U-Net) were also used. Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset were used for training and evaluation. On Inria dataset, RIU-Net achieved the highest IoU score, F1 score, and test accuracy, with 0.6736, 0.7868, and 92.23%, respectively. On Massachusetts Small dataset, Attention Residual U-Net achieved the highest IoU and F1 scores, with 0.6218 and 0.7606, and Trans U-Net reached the highest test accuracy, with 94.26%. On Massachusetts Large dataset, Residual U-Net accomplished the highest IoU and F1 scores, with 0.6165 and 0.7565, and Attention Residual U-Net attained the highest test accuracy, with 93.81%. The results showed that RIU-Net was significantly successful on Inria dataset. On Massachusetts datasets, Residual U-Net, Attention Residual U-Net, and Trans U-Net provided successful results.
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Choi, Keong-Hun, and Jong-Eun Ha. "Edge Detection based-on U-Net using Edge Classification CNN." Journal of Institute of Control, Robotics and Systems 25, no. 8 (August 31, 2019): 684–89. http://dx.doi.org/10.5302/j.icros.2019.19.0119.
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Di Benedetto, Alessandro, Margherita Fiani, and Lucas Matias Gujski. "U-Net-Based CNN Architecture for Road Crack Segmentation." Infrastructures 8, no. 5 (May 6, 2023): 90. http://dx.doi.org/10.3390/infrastructures8050090.
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Many studies on the semantic segmentation of cracks using the machine learning (ML) technique can be found in the relevant literature. To date, the results obtained are quite good, but often the accuracy of the trained model and the results obtained are evaluated using traditional metrics only, and in most cases, the goal is to detect only the occurrence of cracks. Particular attention should be paid to the thickness of the segmented crack since, in road pavement maintenance, the width of the crack is the main parameter and is the one that characterizes the severity levels. The aim of our study is to optimize the crack segmentation process through the implementation of a modified U-Net model-based algorithm. For this, the Crack500 dataset is used, and then the results are compared with those obtained from the U-Net algorithm, which is currently found to be the most accurate and performant in the literature. The results are promising and accurate, as the findings on the shape and width of the segmented cracks are very close to reality.
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Djohar, Muhammad Awaludin, Anita Desiani, Dewi Lestari Dwi Putri, Des Alwine Zayanti, Ali Amran, Irmeilyana Irmeilyana, and Novi Rustiana Dewi. "Segmentasi Citra Hati Menggunakan Metode Convolutional Neural Network dengan Arsitektur U-Net." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (July 23, 2022): 221–34. http://dx.doi.org/10.31289/jite.v6i1.6751.
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IAbnormalities in the liver can be used to identify the occurrence of disorders of the liver, one of which is called liver cancer. To detect abnormalities in the liver, segmentation is needed to take part of the liver that is affected. Segmentation of the liver is usually done manually with x-rays. . This manual detection is quite time consuming to get the results of the analysis. Segmentation is a technique in the image processing process that allocates images into objects and backgrounds. Deep learning applications can be used to help segment medical images. One of the deep learning methods that is widely used for segmentation is U-Net CNN. U-Net CNN has two parts encoder and decoder which are used for image segmentation. This research applies U-Net CNN to segment the liver data image. The performance results of the application of U-Net CNN on the liver image are very goodAccuracy performance obtained is 99%, sensitivity is 99%. The specificity is 99%, the F1-Score is 98%, the Jacard coefficient is 96.46% and the DSC is 98%. The performance achieved from the application of U-Net CNN on average is above 95%, it can be concluded that the application of U-Net CNN is very good and robust in segmenting abnormalities in the liver. This study only discusses the segmentation of the liver image. The results obtained have not been applied to the classification of types of disorders that exist in the liver yet. Further research can apply the segmentation results from the application of U-Net CNN in the problem of classifying types of liver disorders
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Miron, Casian, Laura Ioana Grigoras, Radu Ciucu, and Vasile Manta. "Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture." Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 67, no. 2 (June 1, 2021): 41–52. http://dx.doi.org/10.2478/bipie-2021-0010.
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Abstract The paper presents a new eye image segmentation method used to extract the pupil contour based on the modified U-Net CNN architecture. The analysis was performed using two databases which contain IR images with a spatial resolution of 640x480 pixels. The first database was acquired in our laboratory and contains 400 eye images and the second database is a selection of 400 images from the publicly available CASIA Iris Lamp database. The results obtained by applying the segmentation based on the CNN architecture were compared to manually-annotated ground truth data. The results obtained are comparable to the state of the art. The purpose of the paper is to present the implementation of a robust segmentation algorithm based on the U-Net convolutional neural network that can be used in eye tracking applications such as human computer interface, communication devices for people with disabilities, marketing research or clinical studies. The proposed method improves uppon existing U-Net CNN architectures in terms of efficiency, by reducing the total number of parameters used from 31 millions to 38k. The advantages of using a number of parameters approximatly 815 times lower than the original U-Net CNN architecture are reduced computing resources consumption and a lower inference time.
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Sariturk, Batuhan, Damla Kumbasar, and Dursun Zafer Seker. "Comparative Analysis of Different CNN Models for Building Segmentation from Satellite and UAV Images." Photogrammetric Engineering & Remote Sensing 89, no. 2 (February 1, 2023): 97–105. http://dx.doi.org/10.14358/pers.22-00084r2.
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Building segmentation has numerous application areas such as urban planning and disaster management. In this study, 12 CNN models (U-Net, FPN, and LinkNet using EfficientNet-B5 backbone, U-Net, SegNet, FCN, and six Residual U-Net models) were generated and used for building segmentation. Inria Aerial Image Labeling Data Set was used to train models, and three data sets (Inria Aerial Image Labeling Data Set, Massachusetts Buildings Data Set, and Syedra Archaeological Site Data Set) were used to evaluate trained models. On the Inria test set, Residual-2 U-Net has the highest F1 and Intersection over Union (IoU) scores with 0.824 and 0.722, respectively. On the Syedra test set, LinkNet-EfficientNet-B5 has F1 and IoU scores of 0.336 and 0.246. On the Massachusetts test set, Residual-4 U-Net has F1 and IoU scores of 0.394 and 0.259. It has been observed that, for all sets, at least two of the top three models used residual connections. Therefore, for this study, residual connections are more successful than conventional convolutional layers.
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Erdem, Firat, Nuri Erkin Ocer, Dilek Kucuk Matci, Gordana Kaplan, and Ugur Avdan. "Apricot Tree Detection from UAV-Images Using Mask R-CNN and U-Net." Photogrammetric Engineering & Remote Sensing 89, no. 2 (February 1, 2023): 89–96. http://dx.doi.org/10.14358/pers.22-00086r2.
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Monitoring trees is necessary to manage and take inventory of forests, monitor plants in urban areas, distribute vegetation, monitor change, and establish sensitive and renewable agricultural systems. This study aims to automatically detect, count, and map apricot trees in an orthophoto, covering an area of approximately 48 ha on the ground surface using two different algorithms based on deep learning. Here, Mask region-based convolutional neural network (Mask R-CNN) and U-Net models were run together with a dilation operator to detect apricot trees in UAV images, and the performances of the models were compared. Results show that Mask R-CNN operated in this way performs better in tree detection, counting, and mapping tasks compared to U-Net. Mask R-CNN with the dilation operator achieved a precision of 98.7%, recall of 99.7%, F1 score of 99.1%, and intersection over union (IoU) of 74.8% for the test orthophoto. U-Net, on the other hand, has achieved a recall of 93.3%, precision of 97.2%, F1 score of 95.2%, and IoU of 58.3% when run with the dilation operator. Mask R-CNN was able to produce successful results in challenging areas. U-Net, on the other hand, showed a tendency to overlook existing trees rather than generate false alarms.
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K.Narasimha Rao, Kesani Prudhvidhar Reddy, Gopavarapu Sai Satya Sreekar, and Gade Gopinath Reddy. "Retinal blood vessels segmentation using CNN algorithm." international journal of engineering technology and management sciences 7, no. 3 (2023): 499–504. http://dx.doi.org/10.46647/ijetms.2023.v07i03.70.
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The precise identification of blood vessels in fundus is crucial for diagnosing fundus diseases. In order to address the issues of inaccurate segmentation and low precision in conventional retinal image analysis for segmentation methods, a new approach was developed.The suggested method merges the U-Net and Dense-Net approaches and aims to enhance vascular feature information. To achieve this, the method employs several techniques such asHistogram equalization with limited contrast enhancement, median filtering, normalization of data, and morphological transformation. Furthermore, to correct artifacts, the method utilizes adaptive gamma correction. Next, randomly selected image blocks are utilized as training data to expand the data and enhance the generalization capability. The Dice loss function was optimized using stochastic gradient descent to improve the accuracy of segmentation, and ultimately, the Dense-U-net model was used for performing the segmentation. The algorithm achieved specificity, accuracy, sensitivity, and AUC of 0.9896, 0.9698, 0.7931, and 0.8946 respectively, indicating significant improvement in vessel segmentation accuracy, particularly in identifying small vessels.
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Lutsenko, V. S., and A. E. Shukhman. "SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 216 (June 2022): 40–50. http://dx.doi.org/10.14489/vkit.2022.06.pp.040-050.
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Our study briefly discusses the architectures of convolutional neural networks (CNN), their advantages and disadvantages. The features of the architecture of the convolutional neural network U-net are described. An analysis of the CNN U-net was carried out, based on the analysis, a rationale was given for choosing the CNN U-net as the main architecture for using and building subsequent created and analyzed models of cert neural networks to solve the problem of segmentation of medical images. The analysis of architectures of convolutional neural networks, which can be used as convolutional layers in CNN U-net, has been carried out. Based on the analysis, three architectures of convolutional neural networks were selected and described suitable for use as convolutional layers in CNN U-net. Using CNN U-net and three selected convolutional neural networks (“resnet34”, “inceptionv3” and “vgg16”), three neural network models for medical image segmentation were created. The training and testing of the created models of neural networks was carried out. Based on the results of training and testing, an analysis of the obtained indicators was carried out. Experiments were carried out with each of the three constructed models (segmentation of images from the validation set was performed and segmented images were presented). Based on the testing indicators and empirical data obtained from the results of the experiments, the most suitable neural network model created for solving the problem of medical image segmentation was determined. The algorithm for segmentation of medical images has been improved. An algorithm is described that uses the predictions of all created models of neural networks, which demonstrated a more accurate result than each of the considered models separately.
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Younisse, Remah, Rawan Ghnemat, and Jaafer Al Saraireh. "Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (October 1, 2023): 5406. http://dx.doi.org/10.11591/ijece.v13i5.pp5406-5417.
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<span lang="EN-US">U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets.</span>
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Mukkapati, Naveen, and M. S. Anbarasi. "Brain Tumor Classification Based on Enhanced CNN Model." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 125–30. http://dx.doi.org/10.18280/ria.360114.
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Brain tumor classification is important process for doctors to plan the treatment for patients based on the stages. Various CNN based architecture is applied for the brain tumor classification to improve the classification performance. Existing methods in brain tumor segmentation have the limitations of overfitting and lower efficiency in handling large dataset. In this research, for brain tumor segmentation purpose the enhanced CNN architecture based on U-Net, for pattern analysis purpose RefineNet and for classifying brain tumor purpose SegNet architecture is proposed. The brain tumor benchmark dataset was used to analysis the efficiency of the enhanced CNN model. The U-Net provides good segmentation based on the local and context information of MRI image. The SegNet selects the important features for classification and also reduces the trainable parameters. When compared with the existing methods of brain tumor classification, the enhanced CNN method has the higher performance. The enhanced CNN model has the accuracy of 96.85% and existing CNN with transfer learning has 94.82% accuracy.
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Ohura, Norihiko, Ryota Mitsuno, Masanobu Sakisaka, Yuta Terabe, Yuki Morishige, Atsushi Uchiyama, Takumi Okoshi, Iizaka Shinji, and Akihiko Takushima. "Convolutional neural networks for wound detection: the role of artificial intelligence in wound care." Journal of Wound Care 28, Sup10 (October 1, 2019): S13—S24. http://dx.doi.org/10.12968/jowc.2019.28.sup10.s13.
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Objective: Telemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessment of telemedicine (MAST) has been emphasised. The development of an eHealth-supported wound assessment system using artificial intelligence is awaited. This study explored whether or not wound segmentation of a diabetic foot ulcer (DFU) and a venous leg ulcer (VLU) by a convolutional neural network (CNN) was possible after being educated using sacral pressure ulcer (PU) data sets, and which CNN architecture was superior at segmentation. Methods: CNNs with different algorithms and architectures were prepared. The four architectures were SegNet, LinkNet, U-Net and U-Net with the VGG16 Encoder Pre-Trained on ImageNet (Unet_VGG16). Each CNN learned the supervised data of sacral pressure ulcers (PUs). Results: Among the four architectures, the best results were obtained with U-Net. U-Net demonstrated the second-highest accuracy in terms of the area under the curve (0.997) and a high specificity (0.943) and sensitivity (0.993), with the highest values obtained with Unet_VGG16. U-Net was also considered to be the most practical architecture and superior to the others in that the segmentation speed was faster than that of Unet_VGG16. Conclusion: The U-Net CNN constructed using appropriately supervised data was capable of segmentation with high accuracy. These findings suggest that eHealth wound assessment using CNNs will be of practical use in the future.
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Tong, Xiaozhong, Bei Sun, Junyu Wei, Zhen Zuo, and Shaojing Su. "EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection." Remote Sensing 13, no. 16 (August 12, 2021): 3200. http://dx.doi.org/10.3390/rs13163200.
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Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic object detection. However, existing CNN-based methods with pooling layers may lose the targets in the deep layers and, thus, cannot be directly applied for infrared small target detection. To overcome this problem, we propose an enhanced asymmetric attention (EAA) U-Net. Specifically, we present an efficient and powerful EAA module that uses both same-layer feature information exchange and cross-layer feature fusion to improve feature representation. In the proposed approach, spatial and channel information exchanges occur between the same layers to reinforce the primitive features of small targets, and a bottom-up global attention module focuses on cross-layer feature fusion to enable the dynamic weighted modulation of high-level features under the guidance of low-level features. The results of detailed ablation studies empirically validate the effectiveness of each component in the network architecture. Compared to state-of-the-art methods, the proposed method achieved superior performance, with an intersection-over-union (IoU) of 0.771, normalised IoU (nIoU) of 0.746, and F-area of 0.681 on the publicly available SIRST dataset.
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Liu, Ziqian, Wenbing Wang, Qing Ma, Xianming Liu, and Junjun Jiang. "Rethinking 3D-CNN in Hyperspectral Image Super-Resolution." Remote Sensing 15, no. 10 (May 15, 2023): 2574. http://dx.doi.org/10.3390/rs15102574.
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Recently, CNN-based methods for hyperspectral image super-resolution (HSISR) have achieved outstanding performance. Due to the multi-band property of hyperspectral images, 3D convolutions are natural candidates for extracting spatial–spectral correlations. However, pure 3D CNN models are rare to see, since they are generally considered to be too complex, require large amounts of data to train, and run the risk of overfitting on relatively small-scale hyperspectral datasets. In this paper, we question this common notion and propose Full 3D U-Net (F3DUN), a full 3D CNN model combined with the U-Net architecture. By introducing skip connections, the model becomes deeper and utilizes multi-scale features. Extensive experiments show that F3DUN can achieve state-of-the-art performance on HSISR tasks, indicating the effectiveness of the full 3D CNN on HSISR tasks, thanks to the carefully designed architecture. To further explore the properties of the full 3D CNN model, we develop a 3D/2D mixed model, a popular kind of model prior, called Mixed U-Net (MUN) which shares a similar architecture with F3DUN. Through analysis on F3DUN and MUN, we find that 3D convolutions give the model a larger capacity; that is, the full 3D CNN model can obtain better results than the 3D/2D mixed model with the same number of parameters when it is sufficiently trained. Moreover, experimental results show that the full 3D CNN model could achieve competitive results with the 3D/2D mixed model on a small-scale dataset, suggesting that 3D CNN is less sensitive to data scaling than what people used to believe. Extensive experiments on two benchmark datasets, CAVE and Harvard, demonstrate that our proposed F3DUN exceeds state-of-the-art HSISR methods both quantitatively and qualitatively.
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Jwaid, Wasan M., Zainab Shaker Matar Al-Husseini, and Ahmad H. Sabry. "Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (August 31, 2021): 23–31. http://dx.doi.org/10.15587/1729-4061.2021.238957.
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Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.
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Konovalenko, Ihor, Pavlo Maruschak, Janette Brezinová, Olegas Prentkovskis, and Jakub Brezina. "Research of U-Net-Based CNN Architectures for Metal Surface Defect Detection." Machines 10, no. 5 (April 29, 2022): 327. http://dx.doi.org/10.3390/machines10050327.
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The quality, wear and safety of metal structures can be controlled effectively, provided that surface defects, which occur on metal structures, are detected at the right time. Over the past 10 years, researchers have proposed a number of neural network architectures that have shown high efficiency in various areas, including image classification, segmentation and recognition. However, choosing the best architecture for this particular task is often problematic. In order to compare various techniques for detecting defects such as “scratch abrasion”, we created and investigated U-Net-like architectures with encoders such as ResNet, SEResNet, SEResNeXt, DenseNet, InceptionV3, Inception-ResNetV2, MobileNet and EfficientNet. The relationship between training validation metrics and final segmentation test metrics was investigated. The correlation between the loss function, the , , , and validation metrics and test metrics was calculated. Recognition accuracy was analyzed as affected by the optimizer during neural network training. In the context of this problem, neural networks trained using the stochastic gradient descent optimizer with Nesterov momentum were found to have the best generalizing properties. To select the best model during its training on the basis of the validation metrics, the main test metrics of recognition quality (Dice similarity coefficient) were analyzed depending on the validation metrics. The ResNet and DenseNet models were found to achieve the best generalizing properties for our task. The highest recognition accuracy was attained using the U-Net model with a ResNet152 backbone. The results obtained on the test dataset were and .
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Temenos, Anastasios, Nikos Temenos, Anastasios Doulamis, and Nikolaos Doulamis. "On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net." Technologies 10, no. 1 (January 29, 2022): 19. http://dx.doi.org/10.3390/technologies10010019.
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Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks. In this work, we explore the effectiveness of the CNN-based architecture U-Net and its variations, namely, the Residual U-Net, the Attention U-Net, and the Attention Residual U-Net, in automatic building extraction. We showcase their robustness in feature extraction and information processing using exclusively RGB images, as they are a low-cost alternative to multi-spectral and LiDAR ones, selected from the SpaceNet 1 dataset. The experimental results show that U-Net achieves a 91.9% accuracy, whereas introducing residual blocks, attention gates, or a combination of both improves the accuracy of the vanilla U-Net to 93.6%, 94.0%, and 93.7%, respectively. Finally, the comparison between U-Net architectures and typical deep learning approaches from the literature highlights their increased performance in accurate building localization around corners and edges.
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Fathipoor, H., R. Shah-Hosseini, and H. Arefi. "CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 195–200. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-195-2023.
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Abstract. Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed segmentation in agricultural fields. In this paper, the U-Net++ network, a state-of-the-art convolutional neural network (CNN) algorithm, which has rarely been used in precision agriculture, was implemented for the semantic segmentation of weed images. Then, we compared the model performance to that of the U-Net algorithm based on various criteria.The results show that the U-Net++ outperforms traditional U-Net in terms of overall accuracy, intersection over union (IoU), recall, and F1-Score metrics. Furthermore, the U-Net++ model provided weed IoU of 65%, whereas the U-Net gave weed IoU of 56%. In addition, the results indicate that the U-Net++ is quite capable of detecting small weeds, suggesting that this architecture is more desirable for identifying weeds in the early growing season.
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Wang, An, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Defect Detection of Subway Tunnels Using Advanced U-Net Network." Sensors 22, no. 6 (March 17, 2022): 2330. http://dx.doi.org/10.3390/s22062330.
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In this paper, we present a novel defect detection model based on an improved U-Net architecture. As a semantic segmentation task, the defect detection task has the problems of background–foreground imbalance, multi-scale targets, and feature similarity between the background and defects in the real-world data. Conventionally, general convolutional neural network (CNN)-based networks mainly focus on natural image tasks, which are insensitive to the problems in our task. The proposed method has a network design for multi-scale segmentation based on the U-Net architecture including an atrous spatial pyramid pooling (ASPP) module and an inception module, and can detect various types of defects compared to conventional simple CNN-based methods. Through the experiments using a real-world subway tunnel image dataset, the proposed method showed a better performance than that of general semantic segmentation including state-of-the-art methods. Additionally, we showed that our method can achieve excellent detection balance among multi-scale defects.
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Meng, Yongan, Hailei Lan, Yuqian Hu, Zailiang Chen, Pingbo Ouyang, and Jing Luo. "Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia." Journal of Diabetes Research 2022 (February 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/4612554.
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Objectives.The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose. Methods. The FAZ boundaries on the full-thickness retina of 6 × 6 mm en face OCTA images of DMI and normal eyes were manually marked. Seventy percent of OCTA images were used as the training set, and ten percent of these images were used as the validation set to train the improved U-Net CNN with two attention modules. Finally, twenty percent of the OCTA images were used as the test set to evaluate the accuracy of this model relative to that of the baseline U-Net model. This model was then applied to the public data set sFAZ to compare its effectiveness with existing models at identifying and quantifying the FAZ area. Results. This study included 110 OCTA images. The Dice score of the FAZ area predicted by the proposed method was 0.949, the Jaccard index was 0.912, and the area correlation coefficient was 0.996. The corresponding values for the baseline U-Net were 0.940, 0.898, and 0.995, respectively, and those based on the description data set sFAZ were 0.983, 0.968, and 0.950, respectively, which were better than those previously reported based on this data set. Conclusions. The improved U-Net CNN was more accurate at automatically measuring the FAZ area on the OCTA images than the traditional CNN. The present model may measure the DMI index more accurately, thereby assisting in the diagnosis and prognosis of retinal vascular diseases such as diabetic retinopathy.
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Gunawan, Rudy, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, and Rifai Chai. "Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net." Sensors 22, no. 18 (September 16, 2022): 7031. http://dx.doi.org/10.3390/s22187031.
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Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality.
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М. Ж. Қалдарова, А. С. Аканова, and Н. М. Кашкимбаева. "АУЫЛШАРУАШЫЛЫҚ ЖЕРЛЕРІНІҢ ЕГІС АЛҚАПТАРЫН СЕГМЕНТТЕУДЕ НЕЙРОНДЫҚ ЖЕЛІНІҢ U-NET АРХИТЕКТУРАСЫНЫҢ ҚОЛДАНЫЛУЫ." Bulletin of Toraighyrov University. Energetics series, no. 4.2022 (December 30, 2022): 198–211. http://dx.doi.org/10.48081/kysd9304.
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"Жұмыстың зерттеу нысаны ауылшаруашылық жерлерінің егіс алқаптарын анықтауда ғарыштық суреттерді сегменттеу болып табылады, қазіргі уақытта қарқынды дамуда. Бұл жұмыста қашықтықтан зондтау нәтижесінде алынған ғарыштық (спутниктік) суреттерді сегменттеу арқылы ауылшаруашылық жерлерінің егістік алқаптарын бақылау қарастырылған. Сегменттеудің негізгі әдісі ретінде нейрондық желі, қате функциясы ретінде Intersection Over Union функциясы енгізілген модификацияланған U-net архитектурасы алынған. RGB и NIR арналары үшін орталық блокта біріктірілген екі кодтау блогы пайдаланылды. Intersection Over Union функциясын пайдалану сегменттеу жағдайында нақтырақ мәліметтер алуға көмектеседі және анықтау коэффициенті жоғарылайды. Бастапқы U-net архитектурасына қосымша қабат ретінде Intersection Over Union функциясын қосу ғарыштық суреттердің көмегімен егістік алқаптары жайлы ақпарат және олардың негізгі шекараларын, яғни әр алқаптың белгіленген шекарасын сызып көрсетуге мүмкіндік береді. Бастапқы берілген белгіге сәйкес қиылысу-бұл белгілі бір мәліметтер жиынтығындағы тиісті нысандарды анықтау дәлдігінің өлшемі. Әдетте бұл әдісті HOG + Linear SVM және детекторлы конвуляцияланған нейрондық желілерде (R-CNN, Faster R-CNN, YOLO т.б.) қолданылады. Біздің жағдайымызда U-net архитектурасына қосымша қабат ретінде енгізіп егістік алқаптарын және олардың шекарасын анықтадық. Кілттік сөздер: U-net архитектурасы, алгоритм, сегменттеу, қашықтықтан зондтау, нейрондық желі, машиналық оқыту."
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Hoffman, Jay P., Timothy F. Rahmes, Anthony J. Wimmers, and Wayne F. Feltz. "The Application of a Convolutional Neural Network for the Detection of Contrails in Satellite Imagery." Remote Sensing 15, no. 11 (May 31, 2023): 2854. http://dx.doi.org/10.3390/rs15112854.
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This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to climate change is uncertain and complex. Contrails are found to have a net warming effect because the clouds prevent terrestrial (longwave) radiation from escaping the atmosphere. Globally, this warming effect is greater than the cooling effect the clouds have in the reduction of solar (shortwave) radiation reaching the surface during the daytime. The detection of contrails in satellite imagery is challenging due to their similarity to natural clouds. In this study, a certain type of CNN, U-Net, is used to perform image segmentation in satellite imagery to detect contrails. U-Net can accurately detect contrails with an overall probability of detection of 0.51, a false alarm ratio of 0.46 and a F1 score of 0.52. These results demonstrate the effectiveness of using a U-Net for the detection of contrails in satellite imagery and could be applied to large-scale monitoring of contrail formation to measure their impact on climate change.
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Viedma, Ignacio A., David Alonso-Caneiro, Scott A. Read, and Michael J. Collins. "OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN." Sensors 22, no. 5 (March 4, 2022): 2016. http://dx.doi.org/10.3390/s22052016.
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Optical coherence tomography (OCT) of the posterior segment of the eye provides high-resolution cross-sectional images that allow visualization of individual layers of the posterior eye tissue (the retina and choroid), facilitating the diagnosis and monitoring of ocular diseases and abnormalities. The manual analysis of retinal OCT images is a time-consuming task; therefore, the development of automatic image analysis methods is important for both research and clinical applications. In recent years, deep learning methods have emerged as an alternative method to perform this segmentation task. A large number of the proposed segmentation methods in the literature focus on the use of encoder–decoder architectures, such as U-Net, while other architectural modalities have not received as much attention. In this study, the application of an instance segmentation method based on region proposal architecture, called the Mask R-CNN, is explored in depth in the context of retinal OCT image segmentation. The importance of adequate hyper-parameter selection is examined, and the performance is compared with commonly used techniques. The Mask R-CNN provides a suitable method for the segmentation of OCT images with low segmentation boundary errors and high Dice coefficients, with segmentation performance comparable with the commonly used U-Net method. The Mask R-CNN has the advantage of a simpler extraction of the boundary positions, especially avoiding the need for a time-consuming graph search method to extract boundaries, which reduces the inference time by 2.5 times compared to U-Net, while segmenting seven retinal layers.
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Chan, Huang-Tian, and Chi-Ching Chang. "Decryption of Deterministic Phase-Encoded Digital Holography Using Convolutional Neural Networks." Photonics 10, no. 6 (May 25, 2023): 612. http://dx.doi.org/10.3390/photonics10060612.
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Digital holographic encryption is an important information security technology. Traditional encryption techniques require the use of keys to encrypt information. If the key is lost, it is difficult to recover information, so new technologies that allow legitimate authorized users to access information are necessary. This study encrypts fingerprints and other data using a deterministic phase-encoded encryption system that uses digital holography (DPDH) and determines whether decryption is possible using a convolutional neural network (CNN) using the U-net model. The U-net is trained using a series of ciphertext-plaintext pairs. The results show that the U-net model decrypts and reconstructs images and that the proposed CNN defeats the encryption system. The corresponding plaintext (fingerprint) is retrieved from the ciphertext without using the key so that the proposed method performs well in terms of decryption. The proposed scheme simplifies the decryption process and can be used for information security risk assessment.
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Ghulam, Rehana, Sammar Fatima, Tariq Ali, Nazir Ahmad Zafar, Abdullah A. Asiri, Hassan A. Alshamrani, Samar M. Alqhtani, and Khlood M. Mehdar. "A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor." Computers, Materials & Continua 74, no. 1 (2023): 1333–49. http://dx.doi.org/10.32604/cmc.2023.031695.
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Chen, Dong, Fan Hu, P. Takis Mathiopoulos, Zhenxin Zhang, and Jiju Peethambaran. "MC-UNet: Martian Crater Segmentation at Semantic and Instance Levels Using U-Net-Based Convolutional Neural Network." Remote Sensing 15, no. 1 (January 2, 2023): 266. http://dx.doi.org/10.3390/rs15010266.
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Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from slow convergence and relatively low accuracy. In this paper, we propose a novel CNN, referred to as MC-UNet (Martian Crater U-Net), wherein classical U-Net is employed as the backbone for accurate identification of Martian craters at semantic and instance levels from thermal-emission-imaging-system (THEMIS) daytime infrared images. Compared with classical U-Net, the depth of the layers of MC-UNet is expanded to six, while the maximum number of channels is decreased to one-fourth, thereby making the proposed CNN-based architecture computationally efficient while maintaining a high recognition rate of impact craters on Mars. For enhancing the operation of MC-UNet, we adopt average pooling and embed channel attention into the skip-connection process between the encoder and decoder layers at the same network depth so that large-sized Martian craters can be more accurately recognized. The proposed MC-UNet is adequately trained using 2∼32 km radii Martian craters from THEMIS daytime infrared annotated images. For the predicted Martian crater rim pixels, template matching is subsequently used to recognize Martian craters at the instance level. The experimental results indicate that MC-UNet has the potential to recognize Martian craters with a maximum radius of 31.28 km (136 pixels) with a recall of 0.7916 and F1-score of 0.8355. The promising performance shows that the proposed MC-UNet is on par with or even better than other classical CNN architectures, such as U-Net and Crater U-Net.
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Urase, Yasuyo, Mizuho Nishio, Yoshiko Ueno, Atsushi K. Kono, Keitaro Sofue, Tomonori Kanda, Takaki Maeda, Munenobu Nogami, Masatoshi Hori, and Takamichi Murakami. "Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis." Applied Sciences 10, no. 13 (June 28, 2020): 4446. http://dx.doi.org/10.3390/app10134446.
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The usefulness of sparse-sampling CT with deep learning-based reconstruction for detection of metastasis of malignant ovarian tumors was evaluated. We obtained contrast-enhanced CT images (n = 141) of ovarian cancers from a public database, whose images were randomly divided into 71 training, 20 validation, and 50 test cases. Sparse-sampling CT images were calculated slice-by-slice by software simulation. Two deep-learning models for deep learning-based reconstruction were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) and deeper U-net. For 50 test cases, we evaluated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as quantitative measures. Two radiologists independently performed a qualitative evaluation for the following points: entire CT image quality; visibility of the iliac artery; and visibility of peritoneal dissemination, liver metastasis, and lymph node metastasis. Wilcoxon signed-rank test and McNemar test were used to compare image quality and metastasis detectability between the two models, respectively. The mean PSNR and SSIM performed better with deeper U-net over RED-CNN. For all items of the visual evaluation, deeper U-net scored significantly better than RED-CNN. The metastasis detectability with deeper U-net was more than 95%. Sparse-sampling CT with deep learning-based reconstruction proved useful in detecting metastasis of malignant ovarian tumors and might contribute to reducing overall CT-radiation exposure.
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Zhou, Zhengyin, Zhihui Fu, Juncheng Jia, and Jun Lv. "Rib Fracture Detection with Dual-Attention Enhanced U-Net." Computational and Mathematical Methods in Medicine 2022 (August 18, 2022): 1–13. http://dx.doi.org/10.1155/2022/8945423.
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Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net’s skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.
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Huang, Tinglong, Xuelan Zheng, Lisui He, and Zhiliang Chen. "Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection." Journal of Healthcare Engineering 2021 (November 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5359084.
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The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant ( P < 0.05 ). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant ( P < 0.05 ). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value.
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Chen, Panpan, Chengcheng Liu, Ting Feng, Yong Li, and Dean Ta. "Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network." Applied Sciences 10, no. 22 (November 15, 2020): 8089. http://dx.doi.org/10.3390/app10228089.
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Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection.
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Xu, Cong, Changqing Yu, and Shanwen Zhang. "Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation." Electronics 11, no. 23 (November 29, 2022): 3947. http://dx.doi.org/10.3390/electronics11233947.
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Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their training time is very long. Aiming at the low segmentation accuracy of various diseased leaf images caused by different sizes, colors, shapes, blurred speckle edges and complex backgrounds of traditional U-Net, a lightweight multi-scale extended U-Net (LWMSDU-Net) is constructed for CDLIS. It is composed of encoding and decoding sub-networks. Encoding the sub-network adopts multi-scale extended convolution, the decoding sub-network adopts a deconvolution model, and the residual connection between the encoding module and the corresponding decoding module is employed to fuse the shallow features and deep features of the input image. Compared with the classical U-Net and multi-scale U-Net, the number of layers of LWMSDU-Net is decreased by 1 with a small number of the trainable parameters and less computational complexity, and the skip connection of U-Net is replaced by the residual path (Respath) to connect the encoder and decoder before concatenating. Experimental results on a crop disease leaf image dataset demonstrate that the proposed method can effectively segment crop disease leaf images with an accuracy of 92.17%.
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Zhang, Jiawei, Xin Zhao, Tao Jiang, Md Mamunur Rahaman, Yudong Yao, Yu-Hao Lin, Jinghua Zhang, Ao Pan, Marcin Grzegorzek, and Chen Li. "An Application of Pixel Interval Down-Sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images." Applied Sciences 12, no. 14 (July 21, 2022): 7314. http://dx.doi.org/10.3390/app12147314.
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This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder–decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting.
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Asiri, Abdullah A., Ahmad Shaf, Tariq Ali, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani, Khlood M. Mehdar, et al. "Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications." Life 13, no. 7 (June 26, 2023): 1449. http://dx.doi.org/10.3390/life13071449.
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Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.
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Taher, Fatma, and Neema Prakash. "Automatic cerebrovascular segmentation methods-a review." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 576. http://dx.doi.org/10.11591/ijai.v10.i3.pp576-583.
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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.
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Affane, Abir, Adrian Kucharski, Paul Chapuis, Samuel Freydier, Marie-Ange Lebre, Antoine Vacavant, and Anna Fabijańska. "Segmentation of Liver Anatomy by Combining 3D U-Net Approaches." Applied Sciences 11, no. 11 (May 26, 2021): 4895. http://dx.doi.org/10.3390/app11114895.
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Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.
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Du, Getao, Xu Cao, Jimin Liang, Xueli Chen, and Yonghua Zhan. "Medical Image Segmentation based on U-Net: A Review." Journal of Imaging Science and Technology 64, no. 2 (March 1, 2020): 20508–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020508.
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Abstract Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.
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Adoui, Mahmoudi, Larhmam, and Benjelloun. "MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures." Computers 8, no. 3 (June 29, 2019): 52. http://dx.doi.org/10.3390/computers8030052.
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Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture.
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El Asri, Smail Ait, Samir El Adib, Ismail Negabi, and Naoufal Raissouni. "A Modular System Based on U-Net for Automatic Building Extraction from very high-resolution satellite images." E3S Web of Conferences 351 (2022): 01071. http://dx.doi.org/10.1051/e3sconf/202235101071.
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Recently, convolutional neural networks have grown in popularity in a variety of fields, such as computer vision and audio and text processing. This importance is due to the performance of this type of neural network in the state of the art, and in a wide variety of disciplines. However, the use of convolutional neural networks has not been widely used for remote sensing applications until recently. In this paper, we propose a CNN-based system capable of efficiently extracting buildings from very high-resolution satellite images, by combining the performances of the two architectures; U-Net and VGG19, which is obtained by putting two blocks in parallel based mainly on U-Net: The first block is a standard U-Net, and the second is designed by replacing the contraction path of standard U-Net with the pre-trained weights of VGG19.
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Banu, Syeda Furruka, Md Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Domenec Puig, and Hatem A. Raswan. "AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation." Applied Sciences 11, no. 21 (October 28, 2021): 10132. http://dx.doi.org/10.3390/app112110132.
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Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.
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Nasser, Soraya, Moulkheir Naoui, Ghalem Belalem, and Saïd Mahmoudi. "Semantic Segmentation of Hippocampal Subregions With U-Net Architecture." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–20. http://dx.doi.org/10.4018/ijehmc.20211101.oa4.
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The Automatic semantic segmentation of the hippocampus is an important area of research in which several convolutional neural networks (CNN) models have been used to detect the hippocampus from whole cerebral MRI. In this paper we present two convolutional neural networks the first network ( Hippocampus Segmentation Single Entity HSSE) segmented the hippocampus as a single entity and the second used to detect the hippocampal sub-regions ( Hippocampus Segmentation Multi Class HSMC), these two networks inspire their architecture of the U-net model. Two cohorts were used as training data from (NITRC) (NeuroImaging Tools & Resources Collaboratory (NITRC)) annotated by ITK-SNAP software. We analyze this networks alongside other recent methods that do hippocampal segmentation, the results obtained are encouraging and reach dice scores greater than 0.84
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Feiger, Bradley, Erick Lorenzana-Saldivar, Colin Cooke, Roarke Horstmeyer, Muath Bishawi, Julie Doberne, G. Chad Hughes, David Ranney, Soraya Voigt, and Amanda Randles. "Evaluation of U-Net Based Architectures for Automatic Aortic Dissection Segmentation." ACM Transactions on Computing for Healthcare 3, no. 1 (January 31, 2022): 1–16. http://dx.doi.org/10.1145/3472302.
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Segmentation and reconstruction of arteries is important for a variety of medical and engineering fields, such as surgical planning and physiological modeling. However, manual methods can be laborious and subject to a high degree of human variability. In this work, we developed various convolutional neural network ( CNN ) architectures to segment Stanford type B aortic dissections ( TBADs ), characterized by a tear in the descending aortic wall creating a normal channel of blood flow called a true lumen and a pathologic channel within the wall called a false lumen. We introduced several variations to the two-dimensional ( 2D ) and three-dimensional (3 D ) U-Net, where small stacks of slices were inputted into the networks instead of individual slices or whole geometries. We compared these variations with a variety of CNN segmentation architectures and found that stacking the input data slices in the upward direction with 2D U-Net improved segmentation accuracy, as measured by the Dice similarity coefficient ( DC ) and point-by-point average distance ( AVD ), by more than 15\% . Our optimal architecture produced DC scores of 0.94, 0.88, and 0.90 and AVD values of 0.074, 0.22, and 0.11 in the whole aorta, true lumen, and false lumen, respectively. Altogether, the predicted reconstructions closely matched manual reconstructions.
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Lee, Su Hyun, JiHwan Lee, Kyung-Soo Oh, Jong Pil Yoon, Anna Seo, YoungJin Jeong, and Seok Won Chung. "Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm." PLOS ONE 18, no. 5 (May 18, 2023): e0284111. http://dx.doi.org/10.1371/journal.pone.0284111.
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Introduction Rotator cuff tear (RCT) is a challenging and common musculoskeletal disease. Magnetic resonance imaging (MRI) is a commonly used diagnostic modality for RCT, but the interpretation of the results is tedious and has some reliability issues. In this study, we aimed to evaluate the accuracy and efficacy of the 3-dimensional (3D) MRI segmentation for RCT using a deep learning algorithm. Methods A 3D U-Net convolutional neural network (CNN) was developed to detect, segment, and visualize RCT lesions in 3D, using MRI data from 303 patients with RCTs. The RCT lesions were labeled by two shoulder specialists in the entire MR image using in-house developed software. The MRI-based 3D U-Net CNN was trained after the augmentation of a training dataset and tested using randomly selected test data (training: validation: test data ratio was 6:2:2). The segmented RCT lesion was visualized in a three-dimensional reconstructed image, and the performance of the 3D U-Net CNN was evaluated using the Dice coefficient, sensitivity, specificity, precision, F1-score, and Youden index. Results A deep learning algorithm using a 3D U-Net CNN successfully detected, segmented, and visualized the area of RCT in 3D. The model’s performance reached a 94.3% of Dice coefficient score, 97.1% of sensitivity, 95.0% of specificity, 84.9% of precision, 90.5% of F1-score, and Youden index of 91.8%. Conclusion The proposed model for 3D segmentation of RCT lesions using MRI data showed overall high accuracy and successful 3D visualization. Further studies are necessary to determine the feasibility of its clinical application and whether its use could improve care and outcomes.
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Quenum, Jerome, Iryna V. Zenyuk, and Daniela Ushizima. "Lithium Metal Battery Quality Control via Transformer–CNN Segmentation." Journal of Imaging 9, no. 6 (May 31, 2023): 111. http://dx.doi.org/10.3390/jimaging9060111.
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Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations.
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Shah. Md. Nazmul Arefin, Abu Shahed, Shah Mohd. Ishtiaque Ahammed Khan Ishti, Mst Marium Akter, and Nusrat Jahan. "Deep learning approach for detecting and localizing brain tumor from magnetic resonance imaging images." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1729. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1729-1737.
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<span lang="EN-US">Brain is the most important part of the nervous system. Brain tumor is mainly a mass or growth of abnormal tissues in a brain. Early detection of brain tumor can reduce complex treatment process. Magnetic resonance images (MRI) are used to detect brain tumor. In this paper, we have introduced a deep convolutional neural network (CNN) to automatic brain tumor segmentation using MRI medical images which can solve the vanishing gradient problem. Classifying the brain MRI images with Resnet-50 and InceptionV3 in order to identify whether there is tumor or not. After this step, we have compared the accuracy level of both of the CNN models. Thereafter, applied U-Net architecture individually with encoder Resnet-50 and InceptionV3 to avieved promising results. The publicly available low grade gliomas (LGG) segmentation dataset has been utilized to test the model. Before applying the model on the MRI images preprocessing and several augmentation techniques have been done to obtain quality a dataset. U-net architecture with InceptionV3 provided 99.55% accuracy. On the other hand, our proposed method U-net with encoder ResNet-50 showed 99.77% accuracy.</span>
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Ran, Si, Jianli Ding, Bohua Liu, Xiangyu Ge, and Guolin Ma. "Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation." Sensors 21, no. 5 (March 5, 2021): 1794. http://dx.doi.org/10.3390/s21051794.
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As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) structure, U-Net does not require manual intervention, and its high-precision features are widely used in image interpretation. However, as an end-to-end fully convolutional network, U-Net has not explored enough information from the full scale, and there is still room for improvement. In this study, we constructed an effective network module: residual module under a multisensory field (RMMF) to extract multiscale features of target and an attention mechanism to optimize feature information. RMMF uses parallel convolutional layers to learn features of different scales in the network and adds shortcut connections between stacked layers to construct residual blocks, combining low-level detailed information with high-level semantic information. RMMF is universal and extensible. The convolutional layer in the U-Net network is replaced with RMMF to improve the network structure. Additionally, the multiscale convolutional network was tested using RMMF on the Gaofen-2 data set and Potsdam data sets. Experiments show that compared to other technologies, this method has better performance in airborne and spaceborne images.
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Yeung, Michael, Evis Sala, Carola-Bibiane Schönlieb, and Leonardo Rundo. "Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy." Computers in Biology and Medicine 137 (October 2021): 104815. http://dx.doi.org/10.1016/j.compbiomed.2021.104815.
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Bezmaternykh, P. V., D. A. Ilin, and D. P. Nikolaev. "U-Net-bin: hacking the document image binarization contest." Computer Optics 43, no. 5 (October 2019): 825–32. http://dx.doi.org/10.18287/2412-6179-2019-43-5-825-832.
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Image binarization is still a challenging task in a variety of applications. In particular, Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization. In this work we present a binarization method that was ranked first in the DIBCO`17 contest. It is a convolutional neural network (CNN) based method which uses U-Net architecture, originally designed for biomedical image segmentation. We describe our approach to training data preparation and contest ground truth examination and provide multiple insights on its construction (so called hacking). It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets. A docker container with the final network along with all the supplementary data we used in the training process has been published on Github.
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Pang, Shuyang, Xuewen Xiao, Yuao Cui, Shangwei Mao, Xin Cao, Hongsheng Jia, Hao Wang, Fenghua Tong, and Xiaohui Zhang. "GCN-Unet: A Computer Vision Method with Application to Industrial Granularity Segmentation." Mobile Information Systems 2022 (August 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/1466261.
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In the field of metallurgical industry, identifying the granularity of raw materials is an essential process during transportation. We propose an image segmentation method by the GCN (global convolutional network)-Unet to extract the contour edge of raw materials granules. To obtain legible images of raw materials, a stationary industrial high speed camera is used to photograph the operating belt conveyor from above. Then, a well-trained GCN-Unet model is used to compute the images and output the results with the contour edge of granules and tiny parts of the materials segmented. We combined the U-Net with several global convolutional network models and boundary refinement blocks and compared the prediction results of the GCN-Unet and the U-Net, showing that the GCN-Unet has a better prediction ability with fewer parameters (7,876,675, while the U-Net has 31,101,448 parameters) and a higher calculating speed (about twice faster than the U-Net). Based on the CNN (convolutional neural network), our computer version method can almost replace traditional manual sampling inspection method for the corresponding overall analysis and the automatically identification process.
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Bayrakdar, Ibrahim S., Kaan Orhan, Özer Çelik, Elif Bilgir, Hande Sağlam, Fatma Akkoca Kaplan, Sinem Atay Görür, Alper Odabaş, Ahmet Faruk Aslan, and Ingrid Różyło-Kalinowska. "A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs." BioMed Research International 2022 (January 15, 2022): 1–7. http://dx.doi.org/10.1155/2022/7035367.
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The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.