Improved algorithm for multiple sclerosis diagnosis in MRI using convolutional neural network

Abstract

Magnetic resonance imaging (MRI) is used to diagnose multiple sclerosis (MS) disease lesions in the brain. Diagnosis of MS disease from MRI images is an important and vital thing in today's world. This disease can cause many problems for people who have this disease and reduce the life expectancy in them. So, a strong approach is needed to overcome the challenges in this field. In this study, a method is presented based on convolutional neural networks to detect MS disease lesions from MRI. Four layers of convolution, two layers of pooling, three layers of ReLU are applied, and instead of a fully connected layer, a convolutional layer with a filter size of 1 × 1 has been used to reduce network parameters. Also, for network training, stochastic gradient descent with momentum has been used such that itgreatly improves the speed of learning. Convolutional neural network has a strong potential for MS disease diagnosis and provides good results without the need for lesions segmentation. Also, it has a low sensitivity to the challenges of blurring and different contrasts, and it shows a good performance. The proposed method in this study shows 99.66% accuracy, 99.98% sensitivity and 99.33% specificity.