A Novel Deep Learning Technique for Brain Tumor Detection and Classification Using Parallel CNN with Support Vector Machine ^†

Brain tumors (BT) are also known as intracranial diseases, which occur due to uncontrolled cell growth in the brain. Detecting and classifying the brain tumors at the initial stage is crucial to saving the patient’s life. A radiologist uses MRI scans to identify and classify the various types of BT using a manual approach. However, it is inaccurate and time-consuming because of the many images. In machine learning, convolutional neural networks (CNN) are one significant algorithm that can extract features automatically with high accuracy. The drawback of this algorithm is that it can extract features without knowing micro and macro features. The proposed architecture of parallel CNN (PCNN) can extract the features by knowing the micro and macro features from two separate window sizes and, at first, augmenting the normalized data using geometric transformation to enhance the number of images. Then, micro and macro features are extracted using the proposed architecture, PCNN, alongside batch normalization to reduce the overfitting problem. Finally, three kinds of tumors—glioma, meningioma, pituitary—and a no tumor condition are classified using various classifiers like Softmax, KNN, and SVM. The proposed PCNN-SVM obtained the best accuracy of 96.1% with the special features compared with the other pertained model.