Journal articles: 'CLASSIFICATION OF BRAIN TUMOR'

1

Manasa, P. Venkata Sai, J. Jeevitha, M. Lakshmi Chandana, M. Jeevana Sravanthi, and M. Ali Shaik. "Brain Tumor Radiogenomic Classification Using Deep Learning." International Journal of Research Publication and Reviews 4, no. 3 (March 17, 2023): 1830–36. http://dx.doi.org/10.55248/gengpi.2023.4.33058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

A, Ms Vidhya, Dr Parameswari R, and Ms Sathya S. "Brain Tumor Classification Using Various Machine Learning Algorithms." International Journal of Research in Arts and Science 5, Special Issue (August 30, 2019): 258–70. http://dx.doi.org/10.9756/bp2019.1002/25.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Punam, Saudagar. "Deep Learning Approach for Brain Tumor Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3094–98. http://dx.doi.org/10.22214/ijraset.2021.35648.

Full text

Abstract:

Tumors are complex. There are a lot of variations in sizes and location of tumor. This makes it really hard for complete understanding of tumor. Brain tumour is the abnormal growth of cells inside the brain cranium which limits the functioning of brain. Now a days, medical images processing is a most challenging and developing field. Automated detection of tumor in MRI is extremely crucial because it provides information about abnormal tissues which is important for planning treatment. The conventional method for defect detection in resonance brain images is time consuming. So, automated tumor detection methods are developed because it would save radiologist time and acquire a tested accuracy. The MRI brain tumor detection is complicated task due to complexity and variance of tumors.There are many previously implemented approaches on detecting these kinds of brain tumors. In this paper, we used and implement Convolutional Neural Network (CNN) which is one among the foremost widely used deep learning architectures for classifying a brain tumor into four types. i.e Glioma , Meningioma, Pituitary and No tumour. CNN may be used to effectively locate most cancers cells in brain via MRI. classification.

APA, Harvard, Vancouver, ISO, and other styles

4

Pol, Jay. "Brain Tumor Image Classification using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1934–41. http://dx.doi.org/10.22214/ijraset.2022.44191.

Full text

Abstract:

Abstract: We present a method for segmenting and categorizing brain tumors in the challenge of content of brain tumor with segmentation is enrolled and skull is exposed for bar graph equivalent high-level contradiction refer amount. Preprocessing, segmentation, feature extraction, optimization, and classification are used to detect tumors. The tissue is then classified using preprocessed images. We utilized leave-one-out cross-validation to generate a Dice overlap of 88 for the whole tumor area, 75 for the core tumor region, and 95 for the enhancing tumor region, which is higher than the Dice overlap reported

APA, Harvard, Vancouver, ISO, and other styles

5

Dozic, Slobodan, Dubravka Cvetkovic-Dozic, Milica Skender-Gazibara, and Branko Dozic. "Review of the World Health Organization classification of tumors of the nervous system." Archive of Oncology 10, no. 3 (2002): 175–77. http://dx.doi.org/10.2298/aoo0203175d.

Full text

Abstract:

(Conclusion) Classifications of the nervous system tumors should be neither static nor definitive. The most recent, third, current WHO classification of nervous system tumors was published in 2000. Many substantial changes were introduced. New entities include the chordoid glioma of the third ventricle, the atypical teratoid/rhabdoid tumor, cerebellar liponeurocytoma (the former lipomatous medulloblstoma of the cerebellum), solitary fibrous tumor and perineurioma. The new tumor variants include the large cell medulloblastoma, tanacytic ependymoma and rhabdoid meningioma. Several essential changes were introduced in the meningiomas regarding histological subtypes, grading and proliferation index. In addition to new entities described in the 2000 WHO classification there are newly brain tumor entities and tumor variants, which are not included in the current classification due to the insufficient number of reporeted cases, for example papillary glioneuronal tumor, rosetted glioneuronal tumor, lipoastrocytoma and lipomatous meningioma. They will be probably accepted in the next WHO classificaton. In the current WHO classification the importance of cytogenetic and molecular biologic investigation in the understanding and further classifications of these tumors has been emphasized.

APA, Harvard, Vancouver, ISO, and other styles

6

Narawade, Vaibhav, Chaitali Shetty, Purva Kharsambale, Samruddhi Bhosale, and Sushree Rout. "Brain Tumor Classification using Transfer Learning." Journal of Trends in Computer Science and Smart Technology 5, no. 3 (September 2023): 223–47. http://dx.doi.org/10.36548/jtcsst.2023.3.002.

Full text

Abstract:

Brain tumors are one of the more severe medical conditions that can affect both children and adults. Brain tumors make up between 85 and 90 percent of all primary Central Nervous System (CNS) malignancies. Each year, brain tumors are found in about 11,700 persons. The 5-year survival rate is around 34% for males and 36% for female patients with malignant brain or CNS tumors. Brain tumors can be classified as benign, malignant, pituitary, and other forms. Appropriate treatment, meticulous planning, and exact diagnostics must be used to prolong patient lives. The most reliable way for detecting brain cancer is Magnetic Resonance Imaging (MRI). The images are examined by the radiologist. As brain tumors are complex the MRI serve as guide to diagnose the seriousness of the disease. Since the placement and size of the brain tumor seems incredibly abnormal for persons affected by the disease it becomes difficult to properly comprehend the nature of the tumor. For MRI analysis, a qualified neurosurgeon is also necessary. Compiling the results of an MRI can be extremely difficult and time-consuming because there are typically not enough qualified medical professionals and individuals who are knowledgeable about malignancy in poor countries. Thus, this issue can be resolved by an automated cloud-based solution. In the proposed model, The Convolutional Neural Networks (CNN) is used for the classification of the brain tumor dataset with an accuracy of 99%.

APA, Harvard, Vancouver, ISO, and other styles

7

Wedad Abdul Khuder Naser *. "Brain tumor classification and diagnosis techniques." Global Journal of Engineering and Technology Advances 10, no. 2 (February 28, 2022): 071–74. http://dx.doi.org/10.30574/gjeta.2022.10.2.0036.

Full text

Abstract:

One of the leading causes of increased mortality in both children and adults is a brain tumor. Tumor is a severe issue that has taken over the usual force that controls growth. On MRI pictures, there are several techniques for classification and detecting a brain tumor region. We present background reviews of many proposed techniques for detecting brain tumors in this paper. There is a lot of literature on diagnosing and improving the accuracy of this type of brain tumor.

APA, Harvard, Vancouver, ISO, and other styles

8

Kadam, Ankita. "Brain Tumor Classification using Deep Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 417–26. http://dx.doi.org/10.22214/ijraset.2021.39280.

Full text

Abstract:

Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)

APA, Harvard, Vancouver, ISO, and other styles

9

A., Afreen Habiba. "Diagnosis of Brain Tumor using Semantic Segmentation and Advance-CNN Classification." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1204–24. http://dx.doi.org/10.37200/ijpr/v24i5/pr201795.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Havaei, Mohammad, Hugo Larochelle, Philippe Poulin, and Pierre-Marc Jodoin. "Within-brain classification for brain tumor segmentation." International Journal of Computer Assisted Radiology and Surgery 11, no. 5 (November 3, 2015): 777–88. http://dx.doi.org/10.1007/s11548-015-1311-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Sonawane, Sumedh, Sujit Banne, Aditya Bhor, and Parag Barhate. "Brain Tumor Classification using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1526–28. http://dx.doi.org/10.22214/ijraset.2022.41560.

Full text

Abstract:

Abstract: In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance image scans. The data from multi-modal brain tumor segmentation challenge are utilized which are co-registered and skull stripped, and the histogram matching is performed with a reference volume of high contrast. We are detecting tumor by using preprocessing, segmentation, feature extraction, optimization and lastly classification after that preprocessed image use to classify the tissue. We performed a leave-one out cross-validation and achieved 88 Dice overlap for the complete tumor region, 75 for the core tumor region and 95 for enhancing tumor region, which is higher than the Dice overlap reported. Keywords: Machine Learning, CNN Algorithm, Deep Learning, Classification etc.

APA, Harvard, Vancouver, ISO, and other styles

12

Dave, Himank, Nikhil Kant, Nishank Dave, and Divya Ghorui. "BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (November 1, 2021): 227–38. http://dx.doi.org/10.33564/ijeast.2021.v06i07.037.

Full text

Abstract:

The early detection of the tumor plays an important role in the recovery of the patient. In our proposed model, we have collected MRI scans as it helps with the information about the blood supply inside the brain. Thus, for the recognition of anomaly, for examining the increasing of the ailment, and for the diagnosis, we prepared a data set consisting of various MRI images. We then focused on removing unwanted noise and image enhancement. The image characteristics can be enhanced by using image preprocessing techniques. The image enhancement depends upon different factors like computational time, computational cost, quality of the uncorrupted image, and the techniques used for noise elimination. We have made use of various filters for the image pre-processing. In our next step, image segmentation, an image is divided into several regions. We have implemented different types of segmentation techniques including active contours snakes, fuzzy C means, and regionderived triple thresholding. We have further implemented two hybrid segmentation models and used computer-aided detection techniques. Post-processing of the data is done using artificial bee colony optimization and watershed filtering and extraction. We then classify two images into tumor and non-tumor category using the VGG-16 CNN model. The features of the segmented images were further classified into various types of tumors, including Glioma tumor, Meningioma tumor, Pituitary tumor, and no tumor using one-hot encoding. This approach was further validated using synthetic and real MR image dataset from Kaggle (name of data set), to detect and classify different types of tumor.

APA, Harvard, Vancouver, ISO, and other styles

13

Akbani, Sufiyan Salim, Adeeba Naaz, Nazish Kausar, and Prof Abdul Razzaque. "Brain Tumor Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 573–77. http://dx.doi.org/10.22214/ijraset.2022.41321.

Full text

Abstract:

Abstract: One of the most leading death causes in the world is brain tumor. Tumor Detection is one of the most difficult tasks in medical image processing. In fact, the manual classification with human-assisted support can be improper prediction and diagnosis shown by medical evidence. The detection task is too difficult to perform because there is a lot of diversity in the images as brain tumors come in different shapes and textures. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance imaging (MRI). In this paper, we propose a deep learning model for the classification of brain tumors from MRI images using convolutional neural network (CNN) based on transfer learning. The implemented system explores a number of CNN architectures, image preprocessing and transfer learning model named MobilNet to achieve the better performance and accuracy. Keywords: Deep learning, convolutional neural network, Transfer learning, Brain tumor, medical image classification, MobileNet architecture, etc.

APA, Harvard, Vancouver, ISO, and other styles

14

Ganesh, R., and Dr R. Sivakumar. "Diagnosis of Brain Tumor Using Artificial Neural Network." International Academic Journal of Innovative Research 8, no. 1 (December 20, 2021): 06–10. http://dx.doi.org/10.9756/iajir/v8i1/iajir0802.

Full text

Abstract:

Accurate detection and diagnosis of brain tumor is one the crucial task of medical image analysis. Brain tumor classification system aids the physician to make accurate diagnosis and to provide effective treatment. Magnetic Resonance Imaging (MRI) is the gold standard imaging technique for brain tumor diagnosis. This paper proposes a method for brain tumor detection and classification using artificial neural network. The proposed method consists of four major processes such as preprocessing, region of interest segmentation, feature extraction and classification. Feed forward neural network is employed to classify the brain tumors. Classification performance of the proposed method is evaluated using 10-cross fold validation and compared with the previous methods. Empirical findings proved that the proposed method can efficiently classify the brain tumor with higher classification rate.

APA, Harvard, Vancouver, ISO, and other styles

15

Seetha, J., and S. Selvakumar Raja. "Brain Tumor Classification Using Convolutional Neural Networks." Biomedical and Pharmacology Journal 11, no. 3 (September 19, 2018): 1457–61. http://dx.doi.org/10.13005/bpj/1511.

Full text

Abstract:

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by using small kernels. The weight of the neuron is given as small. Experimental results show that the CNN archives rate of 97.5% accuracy with low complexity and compared with the all other state of arts methods.

APA, Harvard, Vancouver, ISO, and other styles

16

Khan, Farhana, Shahnawaz Ayoub, Yonis Gulzar, Muneer Majid, Faheem Ahmad Reegu, Mohammad Shuaib Mir, Arjumand Bano Soomro, and Osman Elwasila. "MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor." Journal of Imaging 9, no. 8 (August 16, 2023): 163. http://dx.doi.org/10.3390/jimaging9080163.

Full text

Abstract:

The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.

APA, Harvard, Vancouver, ISO, and other styles

17

Nayak, Dillip Ranjan, Neelamadhab Padhy, Pradeep Kumar Mallick, Mikhail Zymbler, and Sachin Kumar. "Brain Tumor Classification Using Dense Efficient-Net." Axioms 11, no. 1 (January 17, 2022): 34. http://dx.doi.org/10.3390/axioms11010034.

Full text

Abstract:

Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The developed network is a variant of EfficientNet with dense and drop-out layers added. Similarly, the authors combined data augmentation with min-max normalization to increase the contrast of tumor cells. The benefit of the dense CNN model is that it can accurately categorize a limited database of pictures. As a result, the proposed approach provides exceptional overall performance. The experimental results indicate that the proposed model was 99.97% accurate during training and 98.78% accurate during testing. With high accuracy and a favorable F1 score, the newly designed EfficientNet CNN architecture can be a useful decision-making tool in the study of brain tumor diagnostic tests.

APA, Harvard, Vancouver, ISO, and other styles

18

Tazeen, Tasmiya, and Mrinal Sarvagya. "Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks." International Journal of Engineering and Advanced Technology 10, no. 6 (August 30, 2021): 23–27. http://dx.doi.org/10.35940/ijeat.f2948.0810621.

Full text

Abstract:

Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.

APA, Harvard, Vancouver, ISO, and other styles

19

Kutlu and Avcı. "A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks." Sensors 19, no. 9 (April 28, 2019): 1992. http://dx.doi.org/10.3390/s19091992.

Full text

Abstract:

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

APA, Harvard, Vancouver, ISO, and other styles

20

Khan, Abdul Hannan, Sagheer Abbas, Muhammad Adnan Khan, Umer Farooq, Wasim Ahmad Khan, Shahan Yamin Siddiqui, and Aiesha Ahmad. "Intelligent Model for Brain Tumor Identification Using Deep Learning." Applied Computational Intelligence and Soft Computing 2022 (January 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/8104054.

Full text

Abstract:

Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. An intelligent system for detecting and classifying brain tumors is essential to help physicians. The novel feature of the study is the division of brain tumors into glioma, meningioma, and pituitary using a hierarchical deep learning method. The diagnosis and tumor classification are significant for the quick and productive cure, and medical image processing using a convolutional neural network (CNN) is giving excellent outcomes in this capacity. CNN uses the image fragments to train the data and classify them into tumor types. Hierarchical Deep Learning-Based Brain Tumor (HDL2BT) classification is proposed with the help of CNN for the detection and classification of brain tumors. The proposed system categorizes the tumor into four types: glioma, meningioma, pituitary, and no-tumor. The suggested model achieves 92.13% precision and a miss rate of 7.87%, being superior to earlier methods for detecting and segmentation brain tumors. The proposed system will provide clinical assistance in the area of medicine.

APA, Harvard, Vancouver, ISO, and other styles

21

Haq, Ejaz Ul, Huang Jianjun, Xu Huarong, Kang Li, and Lifen Weng. "A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI." Computational and Mathematical Methods in Medicine 2022 (August 8, 2022): 1–18. http://dx.doi.org/10.1155/2022/6446680.

Full text

Abstract:

Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model’s performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.

APA, Harvard, Vancouver, ISO, and other styles

22

Al-Zoghby, Aya M., Esraa Mohamed K. Al-Awadly, Ahmad Moawad, Noura Yehia, and Ahmed Ismail Ebada. "Dual Deep CNN for Tumor Brain Classification." Diagnostics 13, no. 12 (June 13, 2023): 2050. http://dx.doi.org/10.3390/diagnostics13122050.

Full text

Abstract:

Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection and identification of tumor type and location are crucial for effective treatment and saving lives. Manual diagnoses are time-consuming and depend on radiologist experts; the increasing number of new cases of brain tumors makes it difficult to process massive and large amounts of data rapidly, as time is a critical factor in patients’ lives. Hence, artificial intelligence (AI) is vital for understanding disease and its various types. Several studies proposed different techniques for BT detection and classification. These studies are on machine learning (ML) and deep learning (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior in self-learning and robust in classification and recognition tasks. This research focuses on classifying three types of tumors using MRI imaging: meningioma, glioma, and pituitary tumors. The proposed DCTN model depends on dual convolutional neural networks with VGG-16 architecture concatenated with custom CNN (convolutional neural networks) architecture. After conducting approximately 22 experiments with different architectures and models, our model reached 100% accuracy during training and 99% during testing. The proposed methodology obtained the highest possible improvement over existing research studies. The solution provides a revolution for healthcare providers that can be used as a different disease classification in the future and save human lives.

APA, Harvard, Vancouver, ISO, and other styles

23

Chowdhury, Asmita. "Improved Accuracy of Brain Tumor Detection Using VGG16." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 856–62. http://dx.doi.org/10.22214/ijraset.2022.47056.

Full text

Abstract:

Abstract: Brain tumor is a very serious problem of human life. In recent years, brain tumors have become one of the leading causes of death among people. It is difficult to identify the tumor itself. Direct detection and classification of brain tumors has the potential to achieve high efficiency and high levels of prognosis. However, it is well known that the accuracy of automatic identification and classification techniques varies from one technique to another and depends on the nature of the image. For the diagnosis and classification of brain tumors, MRI images have been very useful in recent years. MRI images allow us to diagnose brain tumors. This paper highlights the techniques of CNN and has worked upon VGG16 model.

APA, Harvard, Vancouver, ISO, and other styles

24

Malarvizhi, A. B., A. Mofika, M. Monapreetha, and A. M. Arunnagiri. "Brain tumour classification using machine learning algorithm." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012042. http://dx.doi.org/10.1088/1742-6596/2318/1/012042.

Full text

Abstract:

Abstract A Brain tumour is formed by a gradual addition of abnormal cells, and this is one of the major causes of death among other sorts of cancers. It is necessary to classify brain tumor using Magnetic Resonance Imaging (MRI) brain tumor image for treatment because MRI images assist as to detect the smallest defect of the body. This paper aimed to automatically classify brain tumours using a machine learning algorithm. In this work, the input image of the brain was pre-processed using median filter, segmented from the background using thresholding and K-means clustering algorithm and its features were extracted using GLCM. Using the SVM classifier, the brain tumour in the image was detected as either benign or malignant. This image classification process helps the doctors and research scientists to detect the tumour during its early stages, thereby controlling the spread of cancerous cells.

APA, Harvard, Vancouver, ISO, and other styles

25

Anitha, R., K. Sundaramoorthy, D. Suseela, T. Suganya Thevi, S. Selvi, and Mohammad Aljanabi. "Naïve Bayesian Classification Based Glioma Brain Tumor Segmentation Using Grey Level Co-occurrence Matrix Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (May 5, 2023): 203–8. http://dx.doi.org/10.17762/ijritcc.v11i4s.6529.

Full text

Abstract:

Brain tumors vary widely in size and form, making detection and diagnosis difficult. This study's main aim is to identify abnormal brain images., classify them from normal brain images, and then segment the tumor areas from the categorised brain images. In this study, we offer a technique based on the Nave Bayesian classification approach that can efficiently identify and segment brain tumors. Noises are identified and filtered out during the preprocessing phase of tumor identification. After preprocessing the brain image, GLCM and probabilistic properties are extracted. Naive Bayesian classifier is then used to train and label the retrieved features. When the tumors in a brain picture have been categorised, the watershed segmentation approach is used to isolate the tumors. This paper's brain pictures are from the BRATS 2015 data collection. The suggested approach has a classification rate of 99.2% for MR pictures of normal brain tissue and a rate of 97.3% for MR images of aberrant Glioma brain tissue. In this study, we provide a strategy for detecting and segmenting tumors that has a 97.54% Probability of Detection (POD), a 92.18% Probability of False Detection (POFD), a 98.17% Critical Success Index (CSI), and a 98.55% Percentage of Corrects (PC). The recommended Glioma brain tumour detection technique outperforms existing state-of-the-art approaches in POD, POFD, CSI, and PC because it can identify tumour locations in abnormal brain images.

APA, Harvard, Vancouver, ISO, and other styles

26

Trembath, Dimitri, Christopher Ryan Miller, and Arie Perry. "Gray Zones in Brain Tumor Classification." Advances in Anatomic Pathology 15, no. 5 (September 2008): 287–97. http://dx.doi.org/10.1097/pap.0b013e3181836a03.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Saeed, Maryam, Irfan Ahmed Halepoto, Sania Khaskheli, and Mehak Bushra. "Optimization and efficiency analysis of deep learning based brain tumor detection." Mehran University Research Journal of Engineering and Technology 42, no. 2 (April 3, 2023): 188. http://dx.doi.org/10.22581/muet1982.2302.19.

Full text

Abstract:

Brain tumors are spreading very fast across the world. It is one of the aggressive diseases which eventually lead to death if not being detected timely and appropriately. The difficult task for neurologists and radiologists is detecting brain tumor at early stages. However, manually detecting brain tumor from magnetic resonance imaging images is challenging, and susceptible to errors as experienced physician is required for this. To resolve both the concerns, an automated brain tumor detection system is developed for early diagnosis of the disease. In this paper, the diagnosis via MRI images are being done along with classification in terms of its type. The proposed system can specifically classify four brain tumor condition classification like meningioma tumor, pituitary tumor, glioma tumor and no tumor. The convolutional neural network method is used for classification and diagnosis of tumors which has accuracy of about 93.60%. This study is done on a KAGGLE dataset which comprises of 3274 Brain MRI scans. This model can be applied for real time brain tumor detection.

APA, Harvard, Vancouver, ISO, and other styles

28

Bhavani, Mrs R., and Dr K. Vasanth. "Classification of brain tumor using a multistage approach based on RELM and MLBP." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 4 (March 13, 2023): e4. http://dx.doi.org/10.4108/eetpht.v8i4.3082.

Full text

Abstract:

INTRODUCTION: Automatic segmentation and classification of brain tumors help in improvement of treatment which will increase the life of the patient. Tumor may be noncancerous (benign) or cancerous (malignant). Precancerous cells may also form into cancer.OBJECTIVES: Hough CNN is applied for selected section which applies hough casting technique in segmentation. METHODS: A multistage methodof extracting features, with multistage neighbouring is done for emerging an exact brain tumor classifying methodology.RESULTS: In this dataset three types of brain tumors are available they are meningioma, glioma, and pituitary.. CONCLUSION: This paperpresented an efficient brain tumor classification approach which involves multiscale preprocessing, multiscale feature extraction and classification.

APA, Harvard, Vancouver, ISO, and other styles

29

Mhaske, Supriya A., and M. L. Dhore. "Brain Tumor Classification Using Machine Learning Mixed Approach." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 1225–30. http://dx.doi.org/10.22214/ijraset.2022.45533.

Full text

Abstract:

Abstract: In this paper, we propose an effective method using Machine learning for the classification of brain tumor tissues. For successful treatment correct and early detection of brain tumors is essential. Here proposed system is using Convolutional Neural Network for feature extraction and classification. In feature extraction, we reduce the number of features in the dataset by creating new features from the existing ones. Here we recognize the types of tissues using CNN. The pooling layer is used to reduce the spatial resolution of the feature maps. This layer brings down the number of parameters needed for image processing. This paper is focused on helping the radiologist and physician to have a second opinion on the diagnosis. These systems help specialists to perform tumor detection very easily. This study aims to diagnose brain tumors using MRI images.

APA, Harvard, Vancouver, ISO, and other styles

30

Nayeem, Md Abid Hasan, Mehedi Hasan Shakil, Sadia Afrin, Sadah Anjum Shanto, Shadia Jahan Mumu, and Md Mahmudul Hasan Shanto. "A Deep Learning Based Classification Model for the Detection of Brain Tumor using MRI." International Journal of Research and Innovation in Applied Science 07, no. 09 (2022): 37–42. http://dx.doi.org/10.51584/ijrias.2022.7904.

Full text

Abstract:

The diagnosis of a brain tumor requires high accuracy, as even small errors in judgment can lead to critical problems. For this reason, brain tumor segmentation is an important challenge for medical purposes. The wrong classification can lead to worse consequences. Therefore, these must be properly divided into many classes or levels, and this is where multiclass classification comes into play. The latest development of image classification technology has made great progress, and the most popular and better method is considered to be the best in this area is CNN, so this paper uses CNN for the brain tumor classification problem. The proposed model successfully classifies brain images into two distinct categories, namely the absence of tumors indicating that a given brain MRI is free of tumors or the Brain contains Tumor. This model produces an accuracy based on the results of a study that was conducted on a group of volunteers.

APA, Harvard, Vancouver, ISO, and other styles

31

Tiwari, Pallavi, Bhaskar Pant, Mahmoud M. Elarabawy, Mohammed Abd-Elnaby, Noor Mohd, Gaurav Dhiman, and Subhash Sharma. "CNN Based Multiclass Brain Tumor Detection Using Medical Imaging." Computational Intelligence and Neuroscience 2022 (June 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/1830010.

Full text

Abstract:

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

APA, Harvard, Vancouver, ISO, and other styles

32

Sridhar, S. R., M. Akila, and R. Asokan. "Convolutional Gated Recurrent Neural Network Based Automatic Detection and Classification of Brain Tumor using Magnetic Resonance Imaging." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (December 31, 2022): 186–93. http://dx.doi.org/10.17762/ijritcc.v10i2s.5927.

Full text

Abstract:

Magnetic Resonance Imaging (MRI) might be a problematic assignment for tumor fluctuation and complexity because of brain image classification. This work presents the brain tumor classification system using Convolutional Gated Recurrent Neural Network (CGRNN) algorithm based on MRI images. The proposed tumor recognition framework comprises of four stages, to be specific preprocessing, feature extraction, segmentation and classification. Extraction of identified tumor framework features was accomplished utilizing Gray Level Co-occurrence Matrix (GLCM) strategy. At long last, the Convolutional Gated Recurrent Neural Network Classifier has been created to perceive various kinds of brain disease. The proposed framework can be effective in grouping these models and reacting to any variation from the abnormality. The entire framework is isolated into different types of phases: the Learning/Training Phase and the Recognition/Test Phase. A CGRNN classifier under the scholarly ideal separation measurements is utilized to decide the chance of every pixel having a place with the foreground (tumor) and the background. MATLAB software is used in the development of the simulation of the proposed system. The suggested method's simulation results show that the analysis of brain tumours is stable. It shows that the proposed brain tumor classifications are superior to those from brain MRIs than existing brain tumor classifications. The overall accuracy of the proposed system is 98.45%.

APA, Harvard, Vancouver, ISO, and other styles

33

C., Narasimha. "Chaotic Biogeography Rider-Based Neural Network for Brain Tumor Classification Using MRI Images." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 701–19. http://dx.doi.org/10.5373/jardcs/v12sp3/20201309.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Eeshwaroju, Sreenivas, and Praveena Jakula. "Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 29–34. http://dx.doi.org/10.53409/mnaa.jcsit20201305.

Full text

Abstract:

The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.

APA, Harvard, Vancouver, ISO, and other styles

35

Shirgan, Dr S. S., and Kanchan Waghmare. "A REVIEW ON PREDICTIVE BASED BRAIN TUMOR DETECTION TECHNIQUES." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (September 1, 2022): 123–25. http://dx.doi.org/10.33564/ijeast.2022.v07i05.022.

Full text

Abstract:

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. If tumor is detected system classified the tumor and conveys patient the stage of tumor he is probably suffering.

APA, Harvard, Vancouver, ISO, and other styles

36

Akila, V., P. K. Abhilash, P. Bala Venakata Satya Phanindra, J. Pavan Kumar, and A. Kavitha. "Brain Tumors Classification System Using Convolutional Recurrent Neural Network." E3S Web of Conferences 309 (2021): 01075. http://dx.doi.org/10.1051/e3sconf/202130901075.

Full text

Abstract:

The brain is a body organ that controls exercise of the relative multitude of parts of the body. Conceding robotized mind tumors in MRI (Magnetic Reverberation Imaging) is a confounded assignment given size and area variety. This strategy decides a wide range of malignancies in the body. Past techniques devour additional time with less accuracy. A manual assessment can be mistaken because of the degree of intricacies engaged with cerebrum tumors and their properties. However, the above proposition isn’t appropriate for mind tumors because of colossal varieties in size and shape. Our proposed strategy to magnify arrangement performance. First, the expanded tumor district using picture enlargement is utilized to return for capital invested rather than the unique tumor area since it can give hints for tumor types. Second, expanded tumor locale split into progressively refined ring structure subregions. With three-component extraction approaches, employing photographs for information augmentation and rotating photographs at various angles, evaluate the performance of the suggested strategy on a large dataset. Utilizing Convolutional Recurrent Neural Network (CRNN), grouping of the tumor into three categories and thus give a virtual portrayal of exact value.

APA, Harvard, Vancouver, ISO, and other styles

37

Qodri, Krisna Nuresa, Indah Soesanti, and Hanung Adi Nugroho. "Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (June 18, 2021): 21. http://dx.doi.org/10.22146/ijitee.62663.

Full text

Abstract:

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

APA, Harvard, Vancouver, ISO, and other styles

38

Roobini, M. S., T. V. L. Bharathi, T. Aishwaya Sailaja, M. Lakshmi, Anitha Ponraj, and D. Deepa. "Predicting Physico-Chemical Characteristics of Brain Tumour." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3473–77. http://dx.doi.org/10.1166/jctn.2020.9213.

Full text

Abstract:

This research proposes a series of algorithms that work for improved Brain Tumor identification and classification. The Brain Tumor study based on the MRI image will effectively resolve the classification method for diagnosis of brain tumors. There are three stages: Extraction of features, Reduction of features and classification. Extraction function and reduction of functionality used for two algorithms. The extracted characteristics are Mean, Standard deviation, Curtosis, Skewness, Entropy Contrast, Variance, Smoothness, Correlation and Power. The result is then supplied to Support Vector Machine (SVM) for the Benign or Malignant classification of tumours.

APA, Harvard, Vancouver, ISO, and other styles

39

Mahmoud, Amena, Nancy Awadallah Awad, Najah Alsubaie, Syed Immamul Ansarullah, Mohammed S. Alqahtani, Mohamed Abbas, Mohammed Usman, Ben Othman Soufiene, and Abeer Saber. "Advanced Deep Learning Approaches for Accurate Brain Tumor Classification in Medical Imaging." Symmetry 15, no. 3 (February 22, 2023): 571. http://dx.doi.org/10.3390/sym15030571.

Full text

Abstract:

A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients’ chances of survival will be significantly lowered. Checking the patient’s MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model.

APA, Harvard, Vancouver, ISO, and other styles

40

TAM, Akshaya, PrasanthiSreeja P, J. Jayashankari, Aezeden Mohamed, Sodikova Iroda, and V. Vijayan. "Identification of Brain Tumor on Mri images with and without Segmentation using DL Techniques." E3S Web of Conferences 399 (2023): 04049. http://dx.doi.org/10.1051/e3sconf/202339904049.

Full text

Abstract:

Brain cancer is a critical disease that results in the deaths of many individuals. Early detection and classification of brain tumors is essential for effective treatment and improved patient outcomes. However, current manual examination of MRI images for tumor detection can be time-consuming and imprecise. In this project, we propose a computer-based system that utilizes image processing techniques and convolutional neural networks (CNNs) for accurate and efficient brain tumor detection and classification. Our system involves several stages, including image pre-processing, segmentation, feature extraction, and classification. By training a CNN on a large dataset of MRI images with known tumor types, our system can accurately detect and classify brain tumors based on extracted features. The results of our experiments demonstrate the effectiveness of our systemin accurately detecting and classifying brain tumors, with potential to greatly improve the accuracy and speed of diagnosis, and ultimately lead to improved patient outcomes. To explicitly depict the tumor region, we have also added the segmentation procedure.

APA, Harvard, Vancouver, ISO, and other styles

41

Faraz, Nuzhat, Bushra Naz, and Sheraz Memon. "Data Mining Approach for Detection and Classification of Brain Tumor." Mehran University Research Journal of Engineering and Technology 41, no. 1 (January 1, 2022): 53–64. http://dx.doi.org/10.22581/muet1982.2201.06.

Full text

Abstract:

Tumor is a mass or cells inside the brain that grows abnormally in one’s brain. Brain tumor is of two types primary and secondary. Primary tumors are hailed from brain cells and secondary tumors take place from cancer cells spread to one’s brain from other organs like lungs or breast. The Magnetic Resonance Imaging (MRI) is widely used because it gives high resolution and better-quality images. The main problem with the images is the inhomogeneity, unsharp boundaries and irregular noise which affects the results. Inhomogeneity means presence of some irrelevant information that must be removed. Unsharp boundaries are the most common problem in the images, they give blurry effect on the images that is why the information is not clear. To overcome these problems, we use the bilateral filter with the other techniques for the effective detection and segmentation. The proposed framework presents the detection and classification of the brain tumor. Bilateral filter is used to remove noise and preserves details. Bilateral filter is the best to preserve edges, sharpens the boundaries and takes care about the details of the image. By doing segmentation and classification we get the tumor detected.

APA, Harvard, Vancouver, ISO, and other styles

42

Aninditha, Tiara. "Adults brain tumor in Cipto Mangunkusumo General Hospital: A descriptive epidemiology." Romanian Journal of Neurology 20, no. 4 (December 31, 2021): 480–84. http://dx.doi.org/10.37897/rjn.2021.4.13.

Full text

Abstract:

Background. Brain tumor is a rare tumor with low incidence. Although it is a rare tumor, the mortality of brain tumor is disproportionately high. Many countries have already published epidemiology of brain tumor. However, the epidemiology of brain tumor in Indonesia remains to be investigated. This article aimed to provide descriptive epidemiology of brain tumor. Methods. The data of brain tumor acquired from Department of Neurology and Neurosurgery Cipto Mangunkusumo General Hospital from 2014 to 2016. The histopathology classification of primary intracranial tumors was based on WHO classification of CNS tumors 2016 while brain metastasis was classified based on other histopathological types. The variables were analyzed and presented descriptively. Results. There were 316 subjects acquired in this study. Most of the subjects (68%) were women. The mean age of this study was 43.8 (11.7). Most of the subjects (86.1%) had primary histopathology. Lung cancer was the most commonly found brain metastasis in this study (34.1%). Conclusions. This is a pilot study of brain tumor epidemiology in Indonesia. Collaboration with other centers in Indonesia is needed to give a more representative insight of brain tumor in Indonesia.

APA, Harvard, Vancouver, ISO, and other styles

43

Vadivel, M., and R. Ganesan. "Design and Development of 3D Brain MRI System Using Deep Neural Networks." Journal of Medical Imaging and Health Informatics 11, no. 10 (October 1, 2021): 2653–59. http://dx.doi.org/10.1166/jmihi.2021.3855.

Full text

Abstract:

A Brain tumor is otherwise known as intracranial tumor. It is a formation of abnormal cells within the brain. A tumor cells grows continuously in the brain and destroys the cells in that specific region causing brain damage. The main problem in the tumor detection is that some normal brain cells tend to behave as tumor cell which leads to misclassification or unwanted brain surgery. A great challenge for the researchers is to identify the region and appropriate tumor mass. Due to this main reason, automated classifications are acquired for the early detection of brain tumor. In this research work, two standard datasets were used to test the developed classification algorithms. In this study, four different deep learning models were utilized to identify the accurate fit model to classify the brain tumor. From the results, it was observed that googlenet has achieved maximum mean classification accuracy of 98.2%, sensitivity 98.67% and specificity 96.17%. Our proposed model can be used to classify the brain tumor more accurately and effectively.

APA, Harvard, Vancouver, ISO, and other styles

44

Saikat Sundar Pal, Prithwish Raymahapatra, Soumyadeep Paul, Sajal Dolui, Avijit Kumar Chaudhuri, and Sulekha Das. "A Novel Brain Tumor Classification Model Using Machine Learning Techniques." international journal of engineering technology and management sciences 7, no. 2 (2023): 87–98. http://dx.doi.org/10.46647/ijetms.2023.v07i02.011.

Full text

Abstract:

The objective of this research work is to classify brain tumor images into 4 different classes by using Convolutional Neural Network (CNN) algorithm i.e. a deep learning method with VGG16 architecture. The four classes are pituitary, glioma, meningioma, and no tumor. The dataset used for this research is a publicly available MRI Image dataset of brain tumor with 7023 images. The methodology followed in this project includes data pre-processing, model building, and evaluation. The dataset is pre-processed by resizing the images to 64x64 and normalizing the pixel values. The VGG16 architecture is used to build the CNN model, and it is trained on the pre-processed data for 10 epochs with a batch size of 64. The model is evaluated using the area under the operating characteristic curve (AUC) metric of the receiver. The results of this project show that the CNN model with VGG16 architecture achieves an AUC of 0.92 for classifying brain tumor images into four different classes. The model performs best for classifying meningioma with AUC of 0.90, followed by pituitary with AUC of 0.91, glioma with AUC of 0.93, and no tumor with AUC of 0.89. In conclusion, the CNN model with VGG16 architecture is an effective approach for classifying brain tumor images into multiple classes. The model achieves high accuracy in identifying different types of brain tumors, which could potentially aid in early diagnosis and treatment of brain tumors.

APA, Harvard, Vancouver, ISO, and other styles

45

Shiny, K. V., and N. Sugitha. "Effective Brain Tumor Classification on MRI Using Deep Belief-convolutional Neural Network with Pixel Change Detection based on Pixel Mapping Technique." Webology 18, Special Issue 05 (December 8, 2021): 1096–117. http://dx.doi.org/10.14704/web/v18si05/web18288.

Full text

Abstract:

Brain tumor is a kind of cancer, in which tissues in the brain grows rapidly and unevenly in the brains and causes huge threats on human life. Brain tumor is recognized as one of the common dreadful cancers among adults and it also affects the children too. This kind of cancer is categorized into two types, such as benign tumor and malignant tumor. However, benign tumor is curable, whereas recovering of patients whoever affected by malignant tumor has less chance to survive. Nowadays, MR images are usually employed to detect the kinds of brain tumor. Early classification and identification of tumor is significant to treat the tumor and saves the human life from early death. However, the classification of brain tumor and percentage in change detection using pre-operative and post-operative MR images is a very challenging task. In order to overcome such issues, this research proposes a new effective technique for brain tumor classification and determination of pixel change detection using proposed Deep Belief Network (DBN) + Deep Convolutional Neural Network (DCNN). The process involves four phases, such as pre-processing, segmentation, feature extraction, and classification. The combination of DBN + CNN is employed for decision making based on error function. The DBN + CNN are trained utilizing the developed BirCat algorithm. Moreover, the proposed approach achieved a maximum accuracy of 0.957, sensitivity of 0.967, and specificity of 0.918.

APA, Harvard, Vancouver, ISO, and other styles

46

Kumar, Azad. "A Review on Automatic Brain Tumor Classification from MRI Imaging." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5012–19. http://dx.doi.org/10.22214/ijraset.2023.52774.

Full text

Abstract:

Abstract: Brain tumor is an irrepressible development of cells that may spread in different tissues. It can be recognized through Magnetic Resonance Imaging (MRI) which is a non-surgical investigation of organ for diagnosing any disease related to the symptoms. Tumors may be cancerous or non-cancerous or it can be considered as life threatening or less dangerous. A tumor belongs to two distinct categories such as benign or malignant. Here benign tumor that has been detected is considered as the non-cancerous or less dangerous and it does not spread to the other part of the brain. It has solid boundaries or contouring that indicates the particular shade of the tumor but malignant is the cancerous tumor which is highly dangerous and it can be spread to the other part of the brain by itself. The boundaries of the malignant tumor are not solid in appearance, instead of that it appears as faded in nature. The intension of the paper is to review different approaches regarding brain tumor classification and finding out certain flaws present in the existing systems.

APA, Harvard, Vancouver, ISO, and other styles

47

Rasheed, Zahid, Yong-Kui Ma, Inam Ullah, Tamara Al Shloul, Ahsan Bin Tufail, Yazeed Yasin Ghadi, Muhammad Zubair Khan, and Heba G. Mohamed. "Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning." Brain Sciences 13, no. 4 (April 1, 2023): 602. http://dx.doi.org/10.3390/brainsci13040602.

Full text

Abstract:

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.

APA, Harvard, Vancouver, ISO, and other styles

48

Purnama Wibowo, Muhammad Aji, Muhammad Bima Al Fayyadl, Yufis Azhar, and Zamah Sari. "Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 4 (August 22, 2022): 538–47. http://dx.doi.org/10.29207/resti.v6i4.4119.

Full text

Abstract:

A brain tumor is a lump caused by an imperfect cell turnover cycle in the brain and can affect all ages. Brain tumors have 4 grades, namely grades 1 to 2 are benign tumor grades, and grades 3 to 4 are malignant tumor grades. Therefore, early identification of brain tumor disease is very important in providing appropriate treatment and treatment. This study uses a dataset obtained through the Kaggle website titled Brain Tumor Classification (MRI). The number of data is 3264 images with details of Glioma tumors (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and without tumors (500 images). In this study, there are 4 scenarios with different testers. This study proposes the classification of brain tumors using Hyperparameter Tuning and EfficientNet models on MRI images. The EfficientNet model used is the EfficientNetB0 and EfficientNetB7 models with the architecture used are the input layer, GlobalAveragePooling2D layer, dropout layer, and dense layer as well as adding augmentation data to the dataset to manipulate the data in order to improve the results of the proposed model. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. Accuracy results in Scenario 1 are 91%, scenario 2 is 95% accurate, scenario 3 is 95%, and scenario 4 is 98%.

APA, Harvard, Vancouver, ISO, and other styles

49

Kumar, Parasa Rishi, Kavya Bonthu, Boyapati Meghana, Koneru Suvarna Vani, and Prasun Chakrabarti. "Multi-class Brain Tumor Classification and Segmentation using Hybrid Deep Learning Network Model." Scalable Computing: Practice and Experience 24, no. 1 (April 19, 2023): 69–80. http://dx.doi.org/10.12694/scpe.v24i1.2088.

Full text

Abstract:

Brain tumor classification is a significant task for evaluating tumors and selecting the type of treatment as per their classes. Brain tumors are diagnosed using multiple imaging techniques. However, MRI is frequently utilized since it provides greater image quality and uses non-ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently displayed impressive performance, particularly in segmentation and classifying problems. Based on convolutional neural network (CNN), a Hybrid Deep Learning Network (HDLN) model is proposed in this research for classifying multiple types of brain tumors including glioma, meningioma, and pituitary tumors. The Mask RCNN is used for brain tumor classification. We used a squeeze-and-excitation residual network (SE-ResNet) for brain tumor segmentation, which is a residual network (ResNet) with a squeeze-and-excitation block. A publicly available research dataset is used for testing the proposed model for experiment analysis and it obtained an overall accuracy of 98.53%, 98.64% sensitivity and 98.91% specificity. In comparison to the most advanced classification models, the proposed model obtained the best accuracy. For multi-class brain tumor diseases, the proposed HDLN model demonstrated its superiority to the existing approaches.

APA, Harvard, Vancouver, ISO, and other styles

50

Sharma, Arpit Kumar, Amita Nandal, Arvind Dhaka, and Arpana Sinhal. "A Novel Brain Tumor Classification Algorithm using SVM Classifier." International Journal of Emerging Technology and Advanced Engineering 12, no. 11 (November 1, 2022): 175–82. http://dx.doi.org/10.46338/ijetae1122_19.

Full text

Abstract:

This study provides an intelligent classification method for distinguishing between abnormal and normal MRI brain images. Medical pictures like MRI, ECG, and CTscan pictures are vital tools for accurately diagnosing human disease. Whenever a lot of MRIs need to be examined, traditional approach of manual tumour analysis, which relies on visual examination by a physician and radiologist, might lead to inaccurate classification. To remove human mistakes, a proposal is made for an intelligent classification system that responds to the essentials of image classification. Brain tumours are one of the primary causes of human mortality. If a tumor is diagnosed appropriately at an early stage, the chances of survival can be improved. The human brain is studied using the MRI method. The acronym MRI stands for magnetic resonance imaging. In this study, classification strategies based on Support Vector Machines (SVM) are proposed and used to brain imaging categorization. In this research, grayscale, symmetry, and texture features are utilised to extract features from MRI images. The fundamental objective of this study is to offer a decent classification result (improved accuracy and lower error rate) to detect MRI brain tumours with help of SVM. Keywords— Brain tumor, Classification, SVM, MRI.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'CLASSIFICATION OF BRAIN TUMOR'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles