Comparative Analysis of SVM and CNN Techniques for Brain Tumor Detection

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.