Image Segmentation with Priority Based Apposite Feature Extraction Model for Detection of Multiple Sclerosis in MR Images Using Deep Learning Technique

Abstract

Multiple Sclerosis (MS) is a degenerative neurological disease caused by damage to the central nervous system's axons and myelin sheaths. MS lesions alter shape, position, and size over time in patients, therefore radiologists must be vigilant in detecting and evaluating MS lesions appropriately. Magnetic resonance imaging (MRI) has gotten a lot of attention from doctors for diagnosing MS, but there are other ways as well. The information provided by MRI modalities to doctors about the brain's anatomy and function is critical for making an MS diagnosis quickly. For automatic extraction of MS lesions from three-dimensional (3D) MR images, this work introduces a new feature selection approach. The approach described here can be used to treat a variety of MS lesions. MS MRI diagnosis takes a long time, is difficult, and is prone to human mistake. The design of a Computer-Aided Diagnosis System (CADS) based on Artificial Intelligence (AI) to diagnose MS incorporates standard machine learning and deep learning approaches. Traditional machine learning uses trial and error to extract, select, and classify features. Deep learning, on the other hand, uses deep layers with values that are automatically learned. In this research work, a Priority based Apposite Feature Extraction Model with Image Segmentation (PAFEM-IS) is proposed for segmentation and feature extraction. With proposed method, a large number of image attributes can be learned with little effort and bias on the part of the user. Apart from that, by using unlabelled data for feature learning, the classifier training can benefit from the significantly larger amount of generally available unlabelled data. The proposed model is compared with the traditional models and the proposed model exhibits better performance levels.