Classification of multiple sclerosis lesion through Deep Learning analysis of MRI images
https://doi.org/10.17586/2226-1494-2024-24-5-824-833
Abstract
Multiple Sclerosis (MS) is a progressive autoimmune disease affecting the central nervous system, causing communication disruptions between the brain and the body. Early and accurate detection of MS lesions in brain Magnetic Resonance Imaging (MRI) scans is crucial for effective treatment. This paper proposes MSNet, a deep learning-based approach for automatic detection and diagnosis of MS lesions from MRI images, leveraging Convolutional Neural Networks (CNNs) for precise lesion identification and classification. Our methodology involves a comprehensive analysis of MRI datasets, including preprocessing steps such as normalization and lesion segmentation. We propose a novel CNN architecture tailored for MS lesion detection, achieving an accuracy rate of 98.2 % on the test dataset. By incorporating advanced image recognition techniques, our system classifies MS lesions from diverse brain pathologies present in MRI images. The model also highlights MS lesions within the MRI images, aiding neuroradiologists in accurate diagnosis and treatment planning. This study contributes significantly to improving MS diagnosis by providing a reliable and automated tool for lesion detection and classification.
About the Authors
M. Divya
SRM Institute of Science and Technology
India
India
Mathavan Divya - PhD, Researcher
Ramapuram Campus, Chennai, 600089
J. Dhilipan
SRM Institute of Science and Technology
India
India
Jayeseelan Dhilipan - PhD, Professor, Header
Ramapuram Campus, Chennai, 600089
A. Saravanan
Easwari Engineering College
India
India
Appu Saravanan - PhD, Professor
Ramapuram Campus, Chennai
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Review
For citations:
Divya M., Dhilipan J., Saravanan A. Classification of multiple sclerosis lesion through Deep Learning analysis of MRI images. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2024;24(5):824-833. https://doi.org/10.17586/2226-1494-2024-24-5-824-833
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