CNN technique for brain tumor detection and classification using MRI

Abstract

In general, an uncontrolled and sudden growth of cells poses a significant threat to human life, particularly in the brain region. Diagnosis of these tumors by classifying and dividing them to determine the location, structure, and proportion of tumors is a major challenge, despite the strenuous efforts made by researchers in this field. In this study, statistical image processing techniques and computational intelligence were used to suggest several approaches for recognizing brain tumors and cancer. In this research, the CNN algorithm was used for classifying brain cancer images into two categories: cancerous and non-cancerous. The image features are extracted by entering data through the first layer and gradually moving to the other layers until reaching the final layer. In this work, the CNN algorithm, ReLu, and Maxpool are used with three steps of filters (16,32,64), the Adam technique is used for stochastic optimization, and the SoftMax function for classification is implemented. Kaggle dataset for 7023 patient images is used, the network is trained until reach 0verall accuracy 63.74% at epoch 35, with a learning rate of 0.003.