Classification of Brain Tumors from MR Images Using a New CNN Architecture

Abstract

Accurately classifying brain tumors is a crucial factor in combatting, intervening, and treating the disease. By automating the tumor diagnosis process without the involvement of human factors, it is possible to decrease the occurrence of human errors during the diagnosis process. In a new deep convolutional neural network architecture was developed to tackle the brain tumor classification problem, resulting in the successful classification of three distinct types of brain tumors - meningioma, glioma and pituitary. With the propose CNN architecture, a classification accuracy of 98.69% was achieved in brain tumor classification. The recommend model is simple and very fast. It was observed that giving high kernel size and strides values in the first layers and low values in the middle layers of the convolutional layers, and keeping the strides value small in the pooling layer had greatly increased on the model performance. The recommend CNN architecture was compared with studies using the same dataset and transfer learning models in the literature. As a result of these comparisons, high-scoring results were obtained with the recommend model. The classification success achieved by the model is state-of-the-art among stand-alone models.