Comparative Analysis of SVM and CNN Techniques for Brain Tumor Detection
Dinesh M. Barode1, Rupali S. Awhad2, Vijay D. Dhangar3, Seema S. Kawathekar4
1Dinesh M. Barode, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India.
2Rupali S. Awhad, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India.
3Vijay D. Dhangar, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India.
4Dr. Seema S. Kawathekar, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India.
Manuscript received on 08 June 2024 | Revised Manuscript received on 13 June 2024 | Manuscript Accepted on 15 June 2024 | Manuscript published on 30 June 2024. | PP: 27-33 | Volume-13 Issue-7, June 2024 | Retrieval Number: 100.1/ijitee.G990813070624 | DOI: 10.35940/ijitee.G9908.13070624
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A brain tumour is the most common disease on earth, and it is harmful to people. Tumours are the uncontrolled growth of cells and tissues in the human body, specifically in the brain, and are referred to as tumours. The image is acquired using CT scans and Magnetic Resonance Images. The identification of tumours at an early stage is critical and challenging for researchers. A patient comes to the hospital when he starts suffering from pain, headache, omission etc and at that time, if he has a tumor, To recognize the tumor early stage it is very different to identify whether it is benign (non-cancerous) or malignant (cancerous), many techniques or methods are available for detection of tumor here we apply SVM algorithm and CNN on brain Magnetic Resonance Images for classification of a benign or malignant tumor. Here, we propose a system based on the new concept of simple tumour detection, which utilises feature extraction techniques, a segmentation algorithm, and classification. To identify similar patients who have or do not have a brain tumour, as well as to ascertain the type of tumour they have and their tumour sizes. By comparing both SVM & CNN, which technique is more beneficial and which one is better in both? The performance of SVM classifiers is measured in terms of training effectiveness and classification accuracy. With 95% accuracy, it manages the process of categorising brain tumours in MRI scans. The efficacy of training and classification accuracy of the CNN classifier is compared (96.33%). Both methods achieve high accuracy; however, compared to SVM, CNN provides greater accuracy and consumes less time for execution.
Keywords: Brain Tumor, Support Vector Machine, Convolution Neural Network, Digital Image Processing.
Scope of the Article: Image Analysis and Processing