Multiclass convolutional neural network based classification for the diagnosis of brain MRI images

Abstract

Magnetic Resonance Imaging (MRI) modality is commonly used by radiologists in the medical field to diagnose an abnormality in the brain. The manual diagnosis process of grade identification is a challenging task that may lead to a False Negative, False Positive and it is also difficult to diagnose the early stages of abnormality. Manual misprediction may reduce the chance of survival in people with brain abnormalities. Hence, computer-aided diagnosis in additional supports the radiologist to identify the abnormality accurately even in the early stages of a brain tumor. In our proposed system, we have developed a Multi Class Convolutional Neural Network model (MCCNN) to identify the tumor in Brain MRI. In this work, BRATS 2015 and Figshare Data are used. After pre-processing, the feature vector is constructed from MRI using the convolution and pooling layers of CNN. Finally, the softmax layer of CNN identifies the tumor. For analyzing the performance of the proposed MCCNN model, two experiments such as Experiment I and Experiment II are conducted. The designed MCCNN provides less complexity when compared to pre-trained models and other CNN-based networks in literature. It decreases loss value, False Negative Rate, False Positive Rate and increases the accuracy of classification. The proposed MCCNN-based model attained a noteworthy performance with an accuracy of 99% in Experiment I and an accuracy of 96% in Experiment II.