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Sudhakar, Bolleddu, Sikrant, Poornima A, Prasad, Mudarakola Lakshmi, Latha, S Bhargavi, Kumar, Golconda Ravi, Sarika, Sabavath, Shaker Reddy, Pundru Chandra. (1402). Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks. , 16(1), 44-60. doi: 10.22059/jitm.2024.96374
Bolleddu Sudhakar; Poornima A Sikrant; Mudarakola Lakshmi Prasad; S Bhargavi Latha; Golconda Ravi Kumar; Sabavath Sarika; Pundru Chandra Shaker Reddy. "Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks". , 16, 1, 1402, 44-60. doi: 10.22059/jitm.2024.96374
Sudhakar, Bolleddu, Sikrant, Poornima A, Prasad, Mudarakola Lakshmi, Latha, S Bhargavi, Kumar, Golconda Ravi, Sarika, Sabavath, Shaker Reddy, Pundru Chandra. (1402). 'Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks', , 16(1), pp. 44-60. doi: 10.22059/jitm.2024.96374
Sudhakar, Bolleddu, Sikrant, Poornima A, Prasad, Mudarakola Lakshmi, Latha, S Bhargavi, Kumar, Golconda Ravi, Sarika, Sabavath, Shaker Reddy, Pundru Chandra. Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks. , 1402; 16(1): 44-60. doi: 10.22059/jitm.2024.96374

Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks

Journal of Information Technology Management
دوره 16، شماره 1، 2024، صفحه 44-60    اصل مقاله (1.47 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2024.96374
نویسندگان
Bolleddu Sudhakar1؛ Poornima A Sikrant2؛ Mudarakola Lakshmi Prasad3؛ S Bhargavi Latha4؛ Golconda Ravi Kumar5؛ Sabavath Sarika6؛ Pundru Chandra Shaker Reddy* 7
1CSE-AIML, Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India.
2Sri Ramachandra Faculty of Management Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
3Computing Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India.
4School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India.
5Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India.
6Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India.
7School of Computing Science and Artificial Intelligence, SR University, Warangal-506371, Telangana, India.
چکیده
Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.
کلیدواژه‌ها
Brain tumour؛ Magnetic Resonance Images (MRI)؛ Deep Learning؛ CNN؛ SVM؛ Image reorganization
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