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

Content Provider	IET Digital Library
Author	Soltani, Azam Nasri, Saeed
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.
Starting Page	4507
Ending Page	4512
Page Count	6
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0366
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Journal	IET Image Processing
Publisher Date	2020-12-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Computer Vision And Image Processing Technique Convolutional Layer Convolutional Neural Nets Convolutional Neural Network Diseases Feature Extraction Fully Connected Layer Gradient Method Image Segmentation Knowledge Engineering Technique Learning in AI Magnetic Resonance Imaging Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy MRI Image MS Disease Diagnosis MS Disease Lesions Multiple Sclerosis Diagnosis Multiple Sclerosis Disease Lesions Network Parameter Network Training Neural Computing Technique Neurophysiology Optical, Image And Video Signal Processing Optimisation Technique Patient Diagnostic Method And Instrumentation Spectroscopy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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