Automated diagnosis of multi-class brain abnormalities using MRI images: A deep convolutional neural network based method

Abstract Automated detection of multi-class brain abnormalities through magnetic resonance imaging (MRI) has received much attention due to its clinical significance and therefore has become an active area of research over the years. The earlier automated schemes often followed traditional machine learning paradigms, in which the proper choice of features and classifiers has remained a major concern. Therefore, deep learning algorithms have been profoundly applied in various medical imaging applications. In this paper, a deep convolutional neural network (CNN) based automated approach is designed for the diagnosis of multi-class brain abnormalities. The proposed CNN model comprises five layers with learnable parameters: four convolutional layers and one fully-connected layer. The objective of designing such a custom deep network is to achieve greater classification performance with reduced number of parameters. The proposed model is evaluated on two benchmark multi-class brain MRI datasets namely, MD-1 and MD-2. The model achieved a classification accuracy of 100.00% and 97.50% on MD-1 and MD-2 datasets respectively. Moreover, four pre-trained CNN models based on the transfer learning approach have been tested over the same datasets. The comparative analysis with existing schemes indicates the superiority of the proposed method.