Improved particle swarm optimized deep convolutional neural network with super‐pixel clustering for multiple sclerosis lesion segmentation in brain MRI imaging

A central nervous system (CNS) disease affecting the insulating myelin sheaths around the brain axons is called multiple sclerosis (MS). In today's world, MS is extensively diagnosed and monitored using the MRI, because of the structural MRI sensitivity in dissemination of white matter lesions with respect to space and time. The main aim of this study is to propose Multiple Sclerosis Lesion Segmentation in Brain MRI imaging using Optimized Deep Convolutional Neural Network and Super‐pixel Clustering. Three stages included in the proposed methodology are: (a) preprocessing, (b) segmentation of super‐pixel, and (c) classification of super‐pixel. In the first stage, image enhancement and skull stripping is done through performing a preprocessing step. In the second stage, the MS lesion and Non‐MS lesion regions are segmented through applying SLICO algorithm over each slice of the volume. In the fourth stage, a CNN training and classification is performed using this segmented lesion and non‐lesion regions. To handle this complex task, a newly developed Improved Particle Swarm Optimization (IPSO) based optimized convolutional neural network classifier is applied. On clinical MS data, the approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods. The main aim of this study is to propose Multiple Sclerosis Lesion Segmentation in Brain MRI imaging using Optimized Deep Convolutional Neural Network (CNN) and Super‐pixel Clustering. Here, SLICO algorithm and CNN is performed using the segmented lesion and non‐lesion regions. To handle this complex task, a newly developed Improved Particle Swarm Optimization (IPSO) based optimized CNN is applied.