Journal articles on the topic 'VGG -16 convolutional'
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Agus, Minarno Eko, Sasongko Yoni Bagas, Munarko Yuda, Nugroho Adi Hanung, and Zaidah Ibrahim. "Convolutional Neural Network featuring VGG-16 Model for Glioma Classification." JOIV : International Journal on Informatics Visualization 6, no. 3 (September 30, 2022): 660. http://dx.doi.org/10.30630/joiv.6.3.1230.
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Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%.
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Moumen, Rajae, Raddouane Chiheb, and Rdouan Faizi. "Real-time Arabic scene text detection using fully convolutional neural networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (April 1, 2021): 1634. http://dx.doi.org/10.11591/ijece.v11i2.pp1634-1640.
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The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.
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Leong, Mei Chee, Dilip K. Prasad, Yong Tsui Lee, and Feng Lin. "Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition." Applied Sciences 10, no. 2 (January 12, 2020): 557. http://dx.doi.org/10.3390/app10020557.
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This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters than 3D CNNs. We adopt transfer learning from pre-trained 2D CNNs for spatial extraction, followed by temporal encoding, before connecting to 3D convolution layers at the top of the architecture. We construct our fusion architecture, semi-CNN, based on three popular models: VGG-16, ResNets and DenseNets, and compare the performance with their corresponding 3D models. Our empirical results evaluated on the action recognition dataset UCF-101 demonstrate that our fusion of 1D, 2D and 3D convolutions outperforms its 3D model of the same depth, with fewer parameters and reduces overfitting. Our semi-CNN architecture achieved an average of 16–30% boost in the top-1 accuracy when evaluated on an input video of 16 frames.
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AGUSTINA, REGITA, RITA MAGDALENA, and NOR KUMALASARI CAECAR PRATIWI. "Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, no. 2 (April 12, 2022): 446. http://dx.doi.org/10.26760/elkomika.v10i2.446.
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ABSTRAKKanker kulit merupakan penyakit yang ditimbulkan oleh perubahan karakteristik sel penyusun kulit dari normal menjadi ganas, yang menyebabkan sel tersebut membelah secara tidak terkendali dan merusak DNA. Deteksi dini dan diagnosis yang akurat diperlukan untuk membantu masyarakat mengindentifikasi apakah kanker kulit atau hanya kelainan kulit biasa. Pada studi ini, dirancang sebuah sistem yang dapat mengklasifikasi kanker kulit dengan memanfaatkan citra kulit pasien yang kemudian diolah menggunakan metode Convolutional Neural Network (CNN) arsitektur VGG-16. Dataset yang digunakan berupa citra jaringan kanker sebanyak 4000 gambar. Proses diawali dengan input citra, pre-processing, pelatihan model dan pengujian sistem. Hasil terbaik diperoleh pada pengujian tanpa pre-processing CLAHE dan Gaussian filter, dengan menggunakan hyperparameter optimizer SGD, learning rate 0,001, epoch 50 dan batch size 32. Akurasi yang diperoleh sebesar 99,70%, loss 0,0055, presisi 0,9975, recall 0,9975 dan f1-score 0,9950.Kata kunci: Kanker kulit, CNN, VGG-16 ABSTRACTSkin cancer is a disease caused by changes in the characteristics of skin cells from normal to malignant, which causes the cells to divide uncontrollably and damage DNA. Early detection and accurate diagnosis are necessary to help the public identify whether skin cancer or just a common skin disorder. In this study, a system was designed that can classify skin cancer by utilizing images of patients' skin which is then processed using the Convolutional Neural Network (CNN) method of VGG-16 architecture. Dataset used in the form of cancer tissue imagery as many as 4000 images. The process begins with image input, pre-processing, model training and system testing. The best results were obtained on testing without pre-processing CLAHE and Gaussian filters, using hyperparameters, SGD optimizer, learning rate 0.001, epoch 50 and batch size 32. Accuracy obtained by 99.70%, loss 0.0055, precision 0.9975, recall 0.9975 and f1-score 0.9950.Keywords: Skin cancer, CNN, VGG-16
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Cakra, Cakra, Syafruddin Syarif, Hamdan Gani, and Andi Patombongi. "ANALISIS KESEGARAN IKAN MUJAIR DAN IKAN NILA DENGAN METODE CONVOLUTIONAL NEURAL NETWORK." Simtek : jurnal sistem informasi dan teknik komputer 7, no. 2 (August 10, 2022): 74–79. http://dx.doi.org/10.51876/simtek.v7i2.138.
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Dalam riset ini, kami melakukan eksperimen implementasi klasifikasi kesegaran ikan mujair dan ikan nila (segar dan tidak segar) berdasarkan mata ikan menggunakan transfer learning dari enam CNN, yaitu Resnet, Alexnet, Vgg-16, Squeezenet, Densenet dan Inception. Dari hasil eksperimen klasifikasi dua kelas kesegaran ikan mujair menggunakan 451 citra menunjukkan bahwa VGG mencapai kinerja terbaik dibanding arsitektur lainnya dimana akurasi klasifikasi mencapai 73%. Dengan akurasi lebih tinggi dibanding arsitektur lainnya maka Resnet relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan Mujair, sedangkan ikan nila dengan menggunakan 574 citra menunjukkan bahwa VGG mencapai kinerja lebih baik dibanding arsitektur lainnya dengan akurasi klasifikasi mencapai 57,9%, dengan demikan maka VGG relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan Nila.
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劉怡, 劉怡. "Research of Art Point of Interest Recommendation Algorithm Based on Modified VGG-16 Network." 電腦學刊 33, no. 1 (February 2022): 071–85. http://dx.doi.org/10.53106/199115992022023301008.
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<p>Traditional point of interest (POI) recommendation algorithms ignore the semantic context of comment information. Integrating convolutional neural networks into recommendation systems has become one of the hotspots in art POI recommendation research area. To solve the above problems, this paper proposes a new art POI recommendation model based on improved VGG-16. Based on the original VGG-16, the improved VGG-16 method optimizes the fully connection layer and uses transfer learning to share the weight parameters of each layer in VGG-16 pre-training model for subsequent training. The new model fuses the review information and user check-in information to improve the performance of POI recommendation. Experiments on real check-in data sets show that the proposed model has better recommendation performance than other advanced points of interest recommendation methods.</p> <p> </p>
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Kamalova, Yu B., and N. A. Andriyanov. "Recognition of microscopic images of pollen grains using the convolutional neural network VGG-16." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 22, no. 3 (2022): 39–46. http://dx.doi.org/10.14529/ctcr220304.
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The article presents the result of an experiment on the application of transfer learning using the Visual Geometry Group with 16 layers (VGG-16) convolutional neural network in relation to the problem of recognizing pollen grains in images. An analysis of the information-theoretical base on the application of machine learning algorithms to the problem of classifying pollen grains over the past few years has shown the need to develop (apply) a new method for recognizing images of pollen grains obtained using an optical microscope. Currently, automatic classification for pollen identification is becoming a very active area of research. The article substantiates the task of automating the classification of pollen grains. The aim of the study is to analyze the efficiency and accuracy of classifying microscopic images of pollen grains using transfer learning of the VGG-16 convolutional neural network. Transfer learning was performed using the VGG-16 neural network, which has 13 convolutional layers grouped into 5 blocks with pooling and 3 smoothing layers at the output. Since transfer learning is used, the number of training epochs can be chosen to be small. For this network, only the smoothing output layers change, and the feature extraction remains the same as in the classical model. Therefore, it was chosen to use 10 training epochs. Other hyperparameters are as follows: Drop Out regularization with a probability of 50%, optimization method is ADAM, activation function is sigmoid, loss function is cross-entropy, batch size is 32 images. As a result, by adjusting the hyperparameters of the model and using augmentation, it was possible to achieve a share of correct recognitions of about 80%. At the same time, due to the different number of training examples, the particular characteristics of the classes differ somewhat. Thus, the maximum precision and recall reach 94% and 83%, respectively, for the Dandelion type. In the future, studies are planned to use augmentation as a preprocessing to create a balanced sample. By applying the VGG-16 convolutional neural network to the problem of pollen grain image recognition, high accuracy and efficiency of the method were achieved.
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J, Samson Immanuel, Manoj G, and Divya P. S. "Performance Metric Estimation of Fast RCNN with VGG-16 Architecture for Emotional Recognition." International Journal of Applied Mathematics, Computational Science and Systems Engineering 4 (June 25, 2022): 30–38. http://dx.doi.org/10.37394/232026.2022.4.4.
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Faster R-CNN is a state-of-the-art universal object detection approach based on a convolutional neural network that offers object limits and objectness scores at each location in an image at the same time. To hypothesis object locations, state-of-the-art object detection networks rely on region proposal techniques. The accuracy of ML/DL models has been shown to be limited in the past due to a range of issues, including wavelength selection, spatial resolution, and hyper parameter selection and tuning. The goal of this study is to create a new automated emotional detection system based on the CK+ database. Fast R-CNN has lowered the detection network’s operating time, revealing region proposal computation as a bottleneck. We develop a Region Proposal Network (RPN) in this paper that shares full-image convolutional features with the detection network, allowing for almost cost-free region suggestions. The suggested VGG-16 Fast RCNN model obtained user accuracy close to 100 percent in the emotion class, followed by VGG-16 (99.79 percent), Alexnet (98.58 percent), and Googlenet (98.58 percent) (98.32 percent). After extensive hyper parameter tuning for emotional recognition, the generated Fast RCNN VGG-16 model showed an overall accuracy of 99.79 percent, far higher than previously published results.
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Wang, Jiwei, and Man Dang. "Theoretical Model and Implementation Path of Party Building Intelligent Networks in Colleges and Universities from the Perspective of Artificial Intelligence." Mobile Information Systems 2022 (May 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/3926970.
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The paper aims to promote the growth of party building work in colleges and universities to improve school party organization, team management and strengthen party member ideological construction and overall party quality. We design intelligent party member business knowledge learning classrooms using deep learning to improve the quality of party members. First, we develop a convolutional neural network (CNN)-based classroom face recognition system and improve its loss function using the associated theory of the Visual Geometry Group 16 (VGG-16) model. Then, using the Single Shot Multi-Box Detector (SSD), we establish a classroom standing behavior identification system. The experimental results demonstrate that the accuracy rate of the conventional VGG-16 in the face recognition system is 93.5%, while the upgraded VGG-16 is 96.5%, with a 3.2% increase over the baseline models.
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Saleh, Yaser, and Nesreen Otoum. "Road-Type Classification through Deep Learning Networks Fine-Tuning." Journal of Information & Knowledge Management 19, no. 01 (March 2020): 2040020. http://dx.doi.org/10.1142/s0219649220400201.
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Road-type classification is increasingly becoming important to be embedded in interactive maps to provide additional useful information for users. The ubiquity of smartphones supported with high definition cameras offers a rich source of information that can be utilised by machine learning techniques. In this paper, we propose a novel Convolutional Neural Network (CNN)-based approach to classify road types using a collection of publicly available images. To overcome the challenge of having huge dataset to train and test CNNs, our approach employs fine-tuning. We conducted an experiment where the VGG-16, VGG-S and GoogLeNet networks were constructed and fine-tuned with the dataset gathered. Our approach achieved an accuracy of 99% in VGG-16 and 100% in VGG-S, while using the GoogLeNet model produced results up to 98%.
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C, Dr Ramya. "Comparative Performance Evaluation of VGG-16 and Capsnet using Kannada MNIST." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1190–94. http://dx.doi.org/10.22214/ijraset.2021.37378.
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Abstract: Handwriting recognition is an important problem in character recognition. It is much more difficult especially for regional languages such as Kannada. In this regard there has been a recent surge of interest in designing convolutional neural networks (CNNs) for this problem. However, CNNs typically require large amounts of training data and cannot handle input transformations. Capsule networks, which is referred to as capsNets proposed recently to overcome these shortcomings and posed to revolutionize deep learning solutions. Our particular interest in this work is to recognize kannada digit characters, and making capsnet robust to rotation and transformation. In this paper, we focus to achieve the following objectives :1. Explore whether or not capsnet is capable of providing a better fit for the digit images; 2. Adapt and incorporate capsNets for the problem of kannada MNIST digit classification problem at hand; 3. develop a real time application to take handwritten input from the user and recognize the digit; 4. Compare the capsnet with other models on various parameters. Keywords: Capsule Networks, Deep Learning, Convolutional Neural Networks (CNNs), Kannada MNIST, VGG-16
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Filho, Marcelo Luis Rodrigues, and Omar Andres Carmona Cortes. "Efficient Breast Cancer Classification Using Histopathological Images and a Simple VGG." Revista de Informática Teórica e Aplicada 29, no. 1 (January 11, 2022): 102–14. http://dx.doi.org/10.22456/2175-2745.119207.
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Breast cancer is the second most deadly disease worldwide. This severe condition led to 627,000 people dying in 2018. Thus, early detection is critical for improving the patients' lifetime or even curing them. In this context, we can appeal to Medicine 4.0, which exploits machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.
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Zhang, Dejun, Fuquan Ren, Yushuang Li, Lei Na, and Yue Ma. "Pneumonia Detection from Chest X-ray Images Based on Convolutional Neural Network." Electronics 10, no. 13 (June 23, 2021): 1512. http://dx.doi.org/10.3390/electronics10131512.
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Pneumonia has caused significant deaths worldwide, and it is a challenging task to detect many lung diseases such as like atelectasis, cardiomegaly, lung cancer, etc., often due to limited professional radiologists in hospital settings. In this paper, we develop a straightforward VGG-based model architecture with fewer layers. In addition, to tackle the inadequate contrast of chest X-ray images, which brings about ambiguous diagnosis, the Dynamic Histogram Enhancement technique is used to pre-process the images. The parameters of our model are reduced by 97.51% compared to VGG-16, 85.86% compared to Res-50, 83.94% compared to Xception, 51.92% compared to DenseNet121, but increased MobileNet by 4%. However, the proposed model’s performance (accuracy: 96.068%, AUC: 0.99107 with a 95% confidence interval of [0.984, 0.996], precision: 94.408%, recall: 90.823%, F1 score: 92.851%) is superior to the models mentioned above (VGG-16: accuracy, 94.359%, AUC: 0.98928; Res-50: accuracy, 92.821%, AUC, 0.98780; Xception: accuracy, 96.068%, AUC, 0.99623; DenseNet121: accuracy, 87.350%, AUC, 0.99347; MobileNet: accuracy, 95.473%, AUC, 0.99531). The original Pneumonia Classification Dataset in Kaggle is split into three sub-sets, training, validation and test sets randomly at ratios of 70%, 10% and 20%. The model’s performance in pneumonia detection shows that the proposed VGG-based model could effectively classify normal and abnormal X-rays in practice, hence reducing the burden of radiologists.
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Younis, Ayesha, Li Qiang, Charles Okanda Nyatega, Mohammed Jajere Adamu, and Halima Bello Kawuwa. "Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches." Applied Sciences 12, no. 14 (July 20, 2022): 7282. http://dx.doi.org/10.3390/app12147282.
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A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.
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Hiremath, Shivasangaiah V., and Mr P. Yogananth. "COVID-19 Image Classification Using VGG-16 & CNN based on CT Scans." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1477–83. http://dx.doi.org/10.22214/ijraset.2022.42565.
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Abstract: The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to CTscan medical images for the detection of COVID-19. In this proposed work we are going to build two Covid19 Image classification models. Both the model uses Lungs CT Scan images to classify the covid-19. We build the first Classification model using VGG16 Transfer leaning framework and second model using Deep Learning Technique Convolutional Neural Network CNN to classify and diagnose the disease and we able to achieve the best accuracy in both the model. Keywords: Visual Geometry Group-VGG16, Convolutional Neural Network-CNN, CT Images, X-Ray, rTPCR
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Sriram, G., T. R. Ganesh Babu, R. Praveena, and J. V. Anand. "Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture." Molecular & Cellular Biomechanics 19, no. 1 (2022): 29–40. http://dx.doi.org/10.32604/mcb.2022.016966.
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Shu, Chenhao, and Xichuan Hu. "Improved Image Style Transfer Based on VGG-16 Convolutional Neural Network Model." Journal of Physics: Conference Series 2424, no. 1 (January 1, 2023): 012021. http://dx.doi.org/10.1088/1742-6596/2424/1/012021.
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Abstract In the field of computer vision, image style transfer is an important research direction. With the promotion of artificial intelligence, this technology is becoming more and more popular. Compared with the current image style transfer technology based on artificial intelligence, the traditional technology appears to be complex, time-consuming and inefficient. Migration technology is mainly based on images of the current mainstream style VGG - 16 convolution neural network model, adopts TensorFlow framework, in this article to a picture before the smart migration style photo style, texture features such as the methods of improvement, by modifying the style loss function, makes the image content images can transfer a variety of style, Finally, it is verified by relevant experiments.
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Fayemiwo, Michael Adebisi, Toluwase Ayobami Olowookere, Samson Afolabi Arekete, Adewale Opeoluwa Ogunde, Mba Obasi Odim, Bosede Oyenike Oguntunde, Oluwabunmi Omobolanle Olaniyan, et al. "Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset." PeerJ Computer Science 7 (August 3, 2021): e614. http://dx.doi.org/10.7717/peerj-cs.614.
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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.
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Han, Lu, Chongchong Yu, Kaitai Xiao, and Xia Zhao. "A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification." Sensors 19, no. 9 (April 26, 2019): 1960. http://dx.doi.org/10.3390/s19091960.
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This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.
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Ali, Luqman, Fady Alnajjar, Hamad Al Jassmi, Munkhjargal Gocho, Wasif Khan, and M. Adel Serhani. "Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures." Sensors 21, no. 5 (March 1, 2021): 1688. http://dx.doi.org/10.3390/s21051688.
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This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.
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Ren, Rui, Shujuan Zhang, Haixia Sun, and Tingyao Gao. "Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network." Sensors 21, no. 16 (August 5, 2021): 5305. http://dx.doi.org/10.3390/s21165305.
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A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.
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Yazid, Rizq Khairi, and Samsuryadi Samsuryadi. "Detection of Diabetic Retinopathy Using Convolutional Neural Network (CNN)." Computer Engineering and Applications Journal 11, no. 3 (October 1, 2022): 203–13. http://dx.doi.org/10.18495/comengapp.v11i3.406.
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One of the complications of Diabetes Mellitus, namely Diabetic Retinopathy (DR) damages the retina of the eye and has five levels of severity: Normal, Mild, Medium, Severe and Proliferate. If not detected and treated, this complication can lead to blindness. Detection and classification of this disease is still done manually by an ophthalmologist using an image of the patient's eye fundus. Manual detection has the disadvantage that it requires an expert in the field and the process is difficult. This research was conducted by detecting and classifying DR disease using Convolutional Neural Network (CNN). The CNN model was built based on the VGG-16 architecture to study the characteristics of the eye fundus images of DR patients. The model was trained using 4750 images which were rescaled to 256 X 256 size and converted to grayscale using the BT-709 (HDTV) method. The CNN-based software with VGG-16 architecture developed resulted in an accuracy of 62% for the detection and classification of 100 test images based on five DR severity classes. This software produces the highest Sensitivity value in the Normal class at 90% and the largest Specificity value in the Mild class at 97.5%.
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Mohammad, Duaa, Inad Aljarrah, and Moath Jarrah. "Searching surveillance video contents using convolutional neural network." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (April 1, 2021): 1656. http://dx.doi.org/10.11591/ijece.v11i2.pp1656-1665.
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Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.
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Purnama, Nyoman. "Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture." JISA(Jurnal Informatika dan Sains) 5, no. 1 (June 20, 2022): 69–74. http://dx.doi.org/10.31326/jisa.v5i1.1270.
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Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.
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Lee, Ki-Sun, Seok-Ki Jung, Jae-Jun Ryu, Sang-Wan Shin, and Jinwook Choi. "Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs." Journal of Clinical Medicine 9, no. 2 (February 1, 2020): 392. http://dx.doi.org/10.3390/jcm9020392.
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Dental panoramic radiographs (DPRs) provide information required to potentially evaluate bone density changes through a textural and morphological feature analysis on a mandible. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different patients who underwent both skeletal bone mineral density and digital panoramic radiographic examinations at the Korea University Ansan Hospital between 2009 and 2018. Four study groups were used to evaluate the impact of various transfer learning strategies on deep CNN models as follows: a basic CNN model with three convolutional layers (CNN3), visual geometry group deep CNN model (VGG-16), transfer learning model from VGG-16 (VGG-16_TF), and fine-tuning with the transfer learning model (VGG-16_TF_FT). The best performing model achieved an overall area under the receiver operating characteristic of 0.858. In this study, transfer learning and fine-tuning improved the performance of a deep CNN for screening osteoporosis in DPR images. In addition, using the gradient-weighted class activation mapping technique, a visual interpretation of the best performing deep CNN model indicated that the model relied on image features in the lower left and right border of the mandibular. This result suggests that deep learning-based assessment of DPR images could be useful and reliable in the automated screening of osteoporosis patients.
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Tammina, Srikanth. "Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images." International Journal of Scientific and Research Publications (IJSRP) 9, no. 10 (October 6, 2019): p9420. http://dx.doi.org/10.29322/ijsrp.9.10.2019.p9420.
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Pérez-Pérez, Blanca Dalila, Juan Pablo García Vázquez, and Ricardo Salomón-Torres. "Evaluation of Convolutional Neural Networks’ Hyperparameters with Transfer Learning to Determine Sorting of Ripe Medjool Dates." Agriculture 11, no. 2 (February 1, 2021): 115. http://dx.doi.org/10.3390/agriculture11020115.
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Convolutional neural networks (CNNs) have proven their efficiency in various applications in agriculture. In crops such as date, they have been mainly used in the identification and sorting of ripe fruits. The aim of this study was the performance evaluation of eight different CNNs, considering transfer learning for their training, as well as five hyperparameters. The CNN architectures evaluated were VGG-16, VGG-19, ResNet-50, ResNet-101, ResNet-152, AlexNet, Inception V3, and CNN from scratch. Likewise, the hyperparameters analyzed were the number of layers, the number of epochs, the batch size, optimizer, and learning rate. The accuracy and processing time were considered to determine the performance of CNN architectures, in the classification of mature dates’ cultivar Medjool. The model obtained from VGG-19 architecture with a batch of 128 and Adam optimizer with a learning rate of 0.01 presented the best performance with an accuracy of 99.32%. We concluded that the VGG-19 model can be used to build computer vision systems that help producers improve their sorting process to detect the Tamar stage of a Medjool date.
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Xu, Xiao-Yan, Min Shao, Pu-Long Chen, and Qin-Geng Wang. "Tropical Cyclone Intensity Prediction Using Deep Convolutional Neural Network." Atmosphere 13, no. 5 (May 12, 2022): 783. http://dx.doi.org/10.3390/atmos13050783.
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In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric reanalysis, as well Best Track of tropical hurricane data over 2014–2018. In order to explore the interpretability of the model structure, sensitivity experiments were designed with various combinations of predictors. The model test results show that simplified VGG-16 (VGG-16 s) outperforms the other two general models (LeNet-5 and AlexNet). The results of the sensitivity experiments display good consistency with the hypothesis and perceptions, which verifies the validity and reliability of the model. Furthermore, the results also suggest that the importance of predictors varies in different targets. The top three factors that are highly related to TCI are sea surface temperature (SST), temperature at 500 hPa (TEM_500), and the differences in wind speed between 850 hPa and 500 hPa (vertical wind shear speed, VWSS). VWSS, relative humidity (RH), and SST are more significant than MCP. For MWS and SST, TEM_500, and temperature at 850 hPa (TEM_850) outweigh the other variables. This conclusion also implies that deep learning could be an alternative way to conduct intensive and quantitative research.
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Susilo, Arief Broto, and Endang Sugiharti. "Accuracy Enhancement in Early Detection of Breast Cancer on Mammogram Images with Convolutional Neural Network (CNN) Methods using Data Augmentation and Transfer Learning." Journal of Advances in Information Systems and Technology 3, no. 1 (April 14, 2021): 9–16. http://dx.doi.org/10.15294/jaist.v3i1.49012.
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The advancement of computer technology has made it possible for computers to imitate the work of the human brain to make decisions that can be used in the healthcare sector. One of the uses is detecting breast cancer by using Machine Learning to increase the sensitivity and or specificity of detection and diagnosis of the disease. Convolutional Neural Network (CNN) is the most commonly used image analysis and classification method in machine learning. This study aims to improve the accuracy of early detection of breast cancer on mammogram images using the CNN method by adding the Data Augmentation and Transfer method. Learning. This study used a mammography image dataset taken from MIAS 2012. The dataset has seven classes with 322 image samples. The results of accuracy tests of early detection process of breast cancer using CNN by utilizing Data Augmentation and Transfer Learning show several findings: Model VGG-16, Model VGG-19, and ResNet-50 produced the same training accuracy rate of 86%, while for validation accuracy, Model ResNet-50 produced the highest level of accuracy (71%) compared to other models (VGG-16=64%, VGG-19=61%). The use of more image datasets may create better accuracy.
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Bhatt, Shital D., and Himanshu B. Soni. "Improving Classification Accuracy of Pulmonary Nodules using Simplified Deep Neural Network." Open Biomedical Engineering Journal 15, no. 1 (December 31, 2021): 180–89. http://dx.doi.org/10.2174/1874120702115010180.
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Background: Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system. Objectives: We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer. Methods: The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules. Results: The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively. Conclusion: The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.
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Humayun, Mamoona, R. Sujatha, Saleh Naif Almuayqil, and N. Z. Jhanjhi. "A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma." Healthcare 10, no. 6 (June 8, 2022): 1058. http://dx.doi.org/10.3390/healthcare10061058.
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Lung cancer is among the most hazardous types of cancer in humans. The correct diagnosis of pathogenic lung disease is critical for medication. Traditionally, determining the pathological form of lung cancer involves an expensive and time-consuming process investigation. Lung cancer is a leading cause of mortality worldwide, with lung tissue nodules being the most prevalent way for doctors to identify it. The proposed model is based on robust deep-learning-based lung cancer detection and recognition. This study uses a deep neural network as an extraction of features approach in a computer-aided diagnosing (CAD) system to assist in detecting lung illnesses at high definition. The proposed model is categorized into three phases: first, data augmentation is performed, classification is then performed using the pretrained CNN model, and lastly, localization is completed. The amount of obtained data in medical image assessment is occasionally inadequate to train the learning network. We train the classifier using a technique known as transfer learning (TL) to solve the issue introduced into the process. The proposed methodology offers a non-invasive diagnostic tool for use in the clinical assessment that is effective. The proposed model has a lower number of parameters that are much smaller compared to the state-of-the-art models. We also examined the desired dataset’s robustness depending on its size. The standard performance metrics are used to assess the effectiveness of the proposed architecture. In this dataset, all TL techniques perform well, and VGG 16, VGG 19, and Xception for 20 epoch structure are compared. Preprocessing functions as a wonderful bridge to build a dependable model and eventually helps to forecast future scenarios by including the interface at a faster phase for any model. At the 20th epoch, the accuracy of VGG 16, VGG 19, and Xception is 98.83 percent, 98.05 percent, and 97.4 percent.
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Balakrishnan Jayakumari, Bipin Nair, and Amel Thomas Kavana. "Classification of heterogeneous Malayalam documents based on structural features using deep learning models." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (February 1, 2023): 894. http://dx.doi.org/10.11591/ijece.v13i1.pp894-901.
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The proposed work gives a comparative study on performance of various pretrained deep learning models for classifying Malayalam documents such as agreement documents, notebook images, and palm leaves. The documents are classified based on their visual and structural features. The dataset was manually collected from different sources. The method of research proceeds with preprocessing, feature extraction, and classification. The proposed work deals with three fine-tuned deep learning models such as visual geometry group-16 (VGG-16), convolutional neural network (CNN) and AlexNet. The models attained high accuracies of 99.7%, 96%, and 95%, respectively. Among the three models, the fine-tuned VGG-16 model was found to perform better attaining a very high accuracy on the dataset. As a future work, methods to classify the documents based on content as well as spectral features can be developed.
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Mora, Marco, José Naranjo-Torres, and Verónica Aubin. "Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes." Applied Sciences 10, no. 22 (November 11, 2020): 7999. http://dx.doi.org/10.3390/app10227999.
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The writer’s identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (“ S ”, “ ∩ ”, “ C ”, “ ∼ ” and “ U ”) has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance.
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Zhang, Changfan, Dezhi Meng, and Jing He. "VGG-16 Convolutional Neural Network-Oriented Detection of Filling Flow Status of Viscous Food." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 4 (July 20, 2020): 568–75. http://dx.doi.org/10.20965/jaciii.2020.p0568.
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A method is proposed to detect the filling flow status for automatic filling of thick liquid food. The method is based on a convolutional neural network algorithm and it solves the problem of poor accuracy in traditional flow detection devices. An adaptive threshold segmentation algorithm was first used to extract the region of interest for the acquired level image. Next, normalization and augmentation treatment were performed on the extracted images to construct a flow status dataset. A VGG-16 network trained on an ImageNet dataset was then used for isomorphic data-oriented feature migration and parameter tuning to automatically extract features and train the model. The identification accuracy and error rate of the network were verified and the advantages and disadvantages of the proposed method were compared to those of other methods. The experimental results demonstrated that the algorithm effectively detects multi-category flow status information and complies with the requirements for actual production.
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Ullah, Zia, Bilal Ahmad Lodhi, and Jin Hur. "Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG." Energies 13, no. 15 (July 26, 2020): 3834. http://dx.doi.org/10.3390/en13153834.
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Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques.
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Daniel, Jesline, J. T. Anita Rose, F. Sangeetha Francelin Vinnarasi, and Venkatesan Rajinikanth. "VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images." Scanning 2022 (June 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/7733860.
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This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI.
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Liu, Jizhan, Irfan Abbas, and Rana Shahzad Noor. "Development of Deep Learning-Based Variable Rate Agrochemical Spraying System for Targeted Weeds Control in Strawberry Crop." Agronomy 11, no. 8 (July 26, 2021): 1480. http://dx.doi.org/10.3390/agronomy11081480.
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Agrochemical application is an important tool in the agricultural industry for the protection of crops. Agrochemical application with conventional sprayers results in the waste of applied agrochemicals, which not only increases financial losses but also contaminates the environment. Targeted agrochemical sprayers using smart control systems can substantially decrease the chemical input, weed control cost, and destructive environmental contamination. A variable rate spraying system was developed using deep learning methods for the development of new models to classify weeds and to accurately spray on desired weeds target. Laboratory and field experiments were conducted to assess the sprayer performance for weed classification and precise spraying of the target weeds using three classification CNNs (Convolutional Neural Networks) models. The DCNNs models (AlexNet, VGG-16, and GoogleNet) were trained using a dataset containing a total of 12,443 images captured from the strawberry field (4200 images with spotted spurge, 4265 images with Shepherd’s purse, and 4178 strawberry plants). The VGG-16 model attained higher values of precision, recall and F1-score as compared to AlexNet and GoogleNet. Additionally VGG-16 model recorded higher percentage of completely sprayed weeds target (CS = 93%) values. Overall in all experiments, VGG-16 performed better than AlexNet and GoogleNet for real-time weeds target classification and precision spraying. The experiments results revealed that the Sprayer performance decreased with the increase of sprayer traveling speed above 3 km/h. Experimental results recommended that the sprayer with the VGG-16 model can achieve high performance that makes it more ideal for a real-time spraying application. It is concluded that the advanced variable rate spraying system has the potential for spot application of agrochemicals to control weeds in a strawberry field. It can reduce the crop input costs and the environmental pollution risks.
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Mattheuwsen, Lukas, and Maarten Vergauwen. "Manhole Cover Detection on Rasterized Mobile Mapping Point Cloud Data Using Transfer Learned Fully Convolutional Neural Networks." Remote Sensing 12, no. 22 (November 20, 2020): 3820. http://dx.doi.org/10.3390/rs12223820.
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Large-scale spatial databases contain information of different objects in the public domain and are of great importance for many stakeholders. These data are not only used to inventory the different assets of the public domain but also for project planning, construction design, and to create prediction models for disaster management or transportation. The use of mobile mapping systems instead of traditional surveying techniques for the data acquisition of these datasets is growing. However, while some objects can be (semi)automatically extracted, the mapping of manhole covers is still primarily done manually. In this work, we present a fully automatic manhole cover detection method to extract and accurately determine the position of manhole covers from mobile mapping point cloud data. Our method rasterizes the point cloud data into ground images with three channels: intensity value, minimum height and height variance. These images are processed by a transfer learned fully convolutional neural network to generate the spatial classification map. This map is then fed to a simplified class activation mapping (CAM) location algorithm to predict the center position of each manhole cover. The work assesses the influence of different backbone architectures (AlexNet, VGG-16, Inception-v3 and ResNet-101) and that of the geometric information channels in the ground image when commonly only the intensity channel is used. Our experiments show that the most consistent architecture is VGG-16, achieving a recall, precision and F2-score of 0.973, 0.973 and 0.973, respectively, in terms of detection performance. In terms of location performance, our approach achieves a horizontal 95% confidence interval of 16.5 cm using the VGG-16 architecture.
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Rezayi, Sorayya, Niloofar Mohammadzadeh, Hamid Bouraghi, Soheila Saeedi, and Ali Mohammadpour. "Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods." Computational Intelligence and Neuroscience 2021 (November 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/5478157.
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Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2 ∗ 2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.
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Hu, Junping, Shitu Abubakar, Shengjun Liu, Xiaobiao Dai, Gen Yang, and Hao Sha. "Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network." Journal of Sensors 2019 (December 27, 2019): 1–11. http://dx.doi.org/10.1155/2019/7174602.
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Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.
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Demeshko, V. S., and A. I. Фёдоров. "Application of convolutional neural networks in the intelligence security system subsystem." «System analysis and applied information science», no. 2 (August 18, 2020): 46–53. http://dx.doi.org/10.21122/2309-4923-2020-2-46-53.
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The article proposes the structure of an integrated security system for areal facilities. The main subsystems included in its composition are considered. The tasks of the intelligence subsystem for detecting and recognizing ground-based objects of observation in a complex phono-target environment are defined.The task of detecting an object of observation was solved on the basis of a previously proposed algorithm. The disadvantage of this algorithm was the presence of false positives from a flickering complex phono-target environment. To eliminate this drawback, it is proposed to apply a classifier based on the convolutional neural network, which distributes the selected objects to specific classes.The analysis and experimental studies to evaluate the accuracy of recognition of ground objects by convolutional architectures such as VGG-16, VGG-19, Inception v3, ResNet-50, MobileNet. Training and verification of the recognition quality of architecture data was carried out on an experimentally created data set with a human image on a contrasting background and at different ranges. The results obtained indicate the possibility of using a convolutional neural network in the security system and its ability to work in real time.
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Ahmad, Iftikhar, Muhammad Hamid, Suhail Yousaf, Syed Tanveer Shah, and Muhammad Ovais Ahmad. "Optimizing Pretrained Convolutional Neural Networks for Tomato Leaf Disease Detection." Complexity 2020 (September 23, 2020): 1–6. http://dx.doi.org/10.1155/2020/8812019.
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Vegetable and fruit plants facilitate around 7.5 billion people around the globe, playing a crucial role in sustaining life on the planet. The rapid increase in the use of chemicals such as fungicides and bactericides to curtail plant diseases is causing negative effects on the agro-ecosystem. The high scale prevalence of diseases in crops affects the production quantity and quality. Solving the problem of early identification/diagnosis of diseases by exploiting a quick and consistent reliable method will benefit the farmers. In this context, our research work focuses on classification and identification of tomato leaf diseases using convolutional neural network (CNN) techniques. We consider four CNN architectures, namely, VGG-16, VGG-19, ResNet, and Inception V3, and use feature extraction and parameter-tuning to identify and classify tomato leaf diseases. We test the underlying models on two datasets, a laboratory-based dataset and self-collected data from the field. We observe that all architectures perform better on the laboratory-based dataset than on field-based data, with performance on various metrics showing variance in the range 10%–15%. Inception V3 is identified as the best performing algorithm on both datasets.
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Vimal, Chamandeep, and Neeraj Shirivastava. "Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network." International Journal of Computer Applications 183, no. 50 (February 19, 2022): 16–21. http://dx.doi.org/10.5120/ijca2022921700.
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Rizki, Ade Muhammad, and Nola Marina. "KLASIFIKASI KERUSAKAN BANGUNAN SEKOLAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN PRE-TRAINED MODEL VGG-16." Jurnal Ilmiah Teknologi dan Rekayasa 24, no. 3 (2019): 197–206. http://dx.doi.org/10.35760/tr.2019.v24i3.2396.
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Bangunan sekolah merupakan komponen utama penunjang pelaksanaan proses belajar mengajar dan menjadi salah satu faktor penentu peningkatan mutu suatu lembaga pendidikan. Ketersediaan sarana dan prasarana sebagai penunjang kegiatan juga merupakan hal yang penting dalam peningkatan mutu itu sendiri, sehingga dibutuhkan pemeliharaan dan perawatan yang tepat dalam penggunaan bangunan tersebut. Pada proses penggunaanya, banyak bangunan sekolah yang tidak terawat dikarenakan kurangnya perhatian pada kualitas bangunan tersebut, maupun faktor-faktor yang tidak menentu, seperti kesalahan dalam merancang, cuaca, maupun bencana alam. Salah satu upaya yang dilakukan untuk penanganan bangunan rusak adalah dengan dilakukannya rehabilitasi sebagai penentuan penilaian kerusakan bangunan yang salah satunya dengan mengklasifikasi bangunan secara langsung maupun dengan kumpulan citra. Berdasarkan permasalahan tersebut, dibangun sebuah model Convolutional Neural Network (CNN) untuk mengklasifikasi kerusakan bangunan sekolah di Indonesia. Algoritma CNN yang dibangun menggunakan VGG-16 sebagai pre-trained modelnya. Algoritma CNN digunakan karena memiliki performa yang lebih baik untuk mempelajari data citra dibandingkan dengan metode konvensional lainnya. Model ini dilatih dan diuji menggunakan 3000 citra kerusakan bangunan, diantaranya memiliki 3 kelas kerusakan yang masing-masing terdiri dari 1000 citra per kelasnya. Pengujian model menggunakan 200 citra kerusakan bangunan dari setiap kelas kerusakan. Hasil penelitian menghasilkan nilai akurasi terbaik pada proses pelatihan 3000 citra dengan menghasilkan 67,8%.
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Bitto, Abu Kowshir, and Imran Mahmud. "Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach." Bulletin of Electrical Engineering and Informatics 11, no. 4 (August 1, 2022): 2378–87. http://dx.doi.org/10.11591/eei.v11i4.3834.
Full textAbstract:
Among the most important systems in the body is the eyes. Although their small stature, humans are unable to imagine existence without it. The human optic is safe against dust particles by a narrow layer called the conjunctiva. It prevents friction during the opening and shutting of the eye by acting as a lubricant. A cataract is an opacification of the eye's lens. There are various forms of eye problems. Because the visual system is the most important of the four sensory organs, external eye abnormalities must be detected early. The classification technique can be used in a variety of situations. A few of these uses are in the healthcare profession. We use visual geometry group (VGG-16), ResNet-50, and Inception-v3 architectures of convolutional neural networks (CNNs) to distinguish between normal eyes, conjunctivitis eyes, and cataract eyes throughout this paper. With a detection time of 485 seconds, Inception-v3 is the most accurate at detecting eye disease, with a 97.08% accuracy, ResNet-50 performs the second-highest accuracy with 95.68% with 1090 seconds and lastly, VGG-16 performs 95.48% accuracy taking the highest time of 2510 seconds to detect eye diseases.
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Arjmand, Alexandros, Constantinos T. Angelis, Vasileios Christou, Alexandros T. Tzallas, Markos G. Tsipouras, Evripidis Glavas, Roberta Forlano, Pinelopi Manousou, and Nikolaos Giannakeas. "Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples." Applied Sciences 10, no. 1 (December 19, 2019): 42. http://dx.doi.org/10.3390/app10010042.
Full textAbstract:
Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).
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Dghim, Soumaya, Carlos M. Travieso-González, and Radim Burget. "Analysis of the Nosema Cells Identification for Microscopic Images." Sensors 21, no. 9 (April 28, 2021): 3068. http://dx.doi.org/10.3390/s21093068.
Full textAbstract:
The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.
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Adebayo, Segun, Halleluyah Oluwatobi Aworinde, Akinwale O. Akinwunmi, Adebamiji Ayandiji, and Awoniran Olalekan Monsir. "Convolutional neural network-based crop disease detection model using transfer learning approach." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (January 1, 2022): 365. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp365-374.
Full textAbstract:
Crop diseases disrupt the crop's physiological constitution by affecting the crop's natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study for the electronic identification and categorization of photographs of cassava leaves. For feature extraction and classification, the study used databases of cassava images and a deep convolutional neural network model. The methodology of this study retrained the models' current weights for visual geometry group (VGG-16), VGG-19, SqueezeNet, and MobileNet. Accuracy, loss, model complexity, and training time were all taken into consideration when evaluating how well the final layer of CNN models performed when trained on the new cassava image datasets.
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Rezeki, Nofitasari Dwi, Suci Aulia, and Sugondo Hadiyoso. "Severity Classification of Alzheimer Dementia Based on MRI Images Using Deep Neural Network." Revue d'Intelligence Artificielle 36, no. 4 (August 31, 2022): 607–13. http://dx.doi.org/10.18280/ria.360413.
Full textAbstract:
Alzheimer's dementia (AD) is the most common type of dementia, usually characterized by memory loss followed by progressive cognitive decline and functional impairment. AD is one of the leading causes of death and cannot be cured, but proper medical treatment can delay the severity of the disease. Early detection of AD can detect early and prevent the disease from getting worse. So, we need a system that can detect AD as a means of support for the clinical diagnosis. In this study, a system was designed to classify the severity of AD using the Convolutional Neural Network (CNN) method with VGG-16 and VGG-19 modeling. From the simulation results with a total of 4,160 MRI datasets, the highest accuracy rate was 98.28% with VGG-19 architecture using Adam's Optimizer for the classification of 3 classes, namely no dementia (normal), mild dementia, and moderate dementia. It is hoped that this study can support clinical diagnosis in assessing the severity of AD.
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Shen, Yanan, Jingfeng Mao, Aihua Wu, Runda Liu, and Kaijian Zhang. "Optimal Slip Ratio Tracking Integral Sliding Mode Control for an EMB System Based on Convolutional Neural Network Online Road Surface Identification." Electronics 11, no. 12 (June 8, 2022): 1826. http://dx.doi.org/10.3390/electronics11121826.
Full textAbstract:
As the main branch of the brake-by-wire system, the electro-mechanical brake (EMB) system is the future direction of vehicle brake systems. In order to enhance the vehicle braking effect and improve driver safety, a convolutional neural network (CNN) online road surface identification algorithm and an optimal slip ratio tracking integral sliding mode controller (ISMC) combined EMB braking control strategy is proposed in this paper. Firstly, according to the quarter-vehicle model and Burckhardt tire model, the vehicle braking control theory based on the optimal slip ratio is analyzed. Secondly, using the VGG-16 CNN method, an online road surface identification algorithm is proposed. Through a comparative study under the same dataset conditions, it is verified that the VGG-16 method has a higher identification accuracy rate than the SVM method. In order to further improve the generalization ability of VGG-16 CNN image identification, data enhancement is performed on the road surface image data training set, including image flipping, clipping, and adjusting sensitivity. Then, combined with the EMB system model, an exponential approach law method-based ISMC is designed to achieve the optimal slip ratio tracking control of the vehicle braking process. Finally, MATLAB/Simulink software is used to verify the correctness and effectiveness of the proposed strategy and shows that the strategy of real-time identifying road surface conditions through vision can make the optimal slip ratio of vehicle braking control reasonably adjusted, so as to ensure that the adhesion coefficient of wheel braking always reaches the peak value, and finally achieves the effect of rapid braking.