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
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© 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 tumor is the most common disease on earth and it is harmful to people. Tumors are the uncontrolled growth of cells and tissues in the human brain called a tumor. The image is acquired using CT scans and Magnetic Resonance Images. The identification of tumors 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 tumor detection that uses feature extraction techniques, segmentation algorithm and classification. To identify similar patients who have or do not have a brain tumor, as well as to ascertain the type of tumor they have and their tumor 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 brain tumor categorization in MRI scans. The efficacy of training and classification accuracy of the CNN classifier is compared (96.33%). Both methods get high accuracy but as compared to SVM, CNN provides more 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