Using SVM and CNN as Image Classifiers for Brain Tumor Dataset

Abstract

Brain tumors make up 85% to 90% of all primary central nervous system (CNS) malignancies. Over a thousand people are diagnosed with cancer each year, and brain tumors are one of those fatal illnesses. It is challenging to diagnose this because of the intricate anatomy of the brain. Medical image processing is expanding rapidly today as it aids in the diagnosis and treatment of illnesses. Initially, a limited dataset was utilized to develop a support vector machine (SVM) model for the classification of brain tumors. The tumors were classified as either present or absent. As the dataset was small, the SVM model achieved great accuracy. To increase the dataset's size, data augmentation, an image pre-processing technique was used. Due to the SVM's limitations in producing high accuracy over a large dataset, convolutional neural network (CNN) was used to produce a more accurate model. Using both SVM and CNN aided in drawing comparisons between deep learning techniques and conventional machine learning techniques. MRI scans were used for tumor classification using the mentioned models.