Diagnosis of Multiple Sclerosis Disease in Brain MRI Images using Convolutional Neural Networks based on Wavelet Pooling
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JADM-9-2_003
تاریخ نمایه سازی: 20 مرداد 1400
Abstract:
Multiple Sclerosis (MS) is a disease that destructs the central nervous system cell protection, destroys sheaths of immune cells, and causes lesions. Examination and diagnosis of lesions by specialists is usually done manually on Magnetic Resonance Imaging (MRI) images of the brain. Factors such as small sizes of lesions, their dispersion in the brain, similarity of lesions to some other diseases, and their overlap can lead to the misdiagnosis. Automatic image detection methods as auxiliary tools can increase the diagnosis accuracy. To this end, traditional image processing methods and deep learning approaches have been used. Deep Convolutional Neural Network is a common method of deep learning to detect lesions in images. In this network, the convolution layer extracts the specificities; and the pooling layer decreases the specificity map size. The present research uses the wavelet-transform-based pooling. In addition to decomposing the input image and reducing its size, the wavelet transform highlights sharp changes in the image and better describes local specificities. Therefore, using this transform can improve the diagnosis. The proposed method is based on six convolutional layers, two layers of wavelet pooling, and a completely connected layer that had a better amount of accuracy than the studied methods. The accuracy of ۹۸.۹۲%, precision of ۹۹.۲۰%, and specificity of ۹۸.۳۳% are obtained by testing the image data of ۳۸ patients and ۲۰ healthy individuals.
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Authors
A. Alijamaat
Computer Engineering Department, Rasht Branch, Islamic Azad University, Rasht, Iran.
A. NikravanShalmani
Computer Engineering Department, Karaj Branch, Islamic Azad University, Karaj, Iran.
P. Bayat
Computer Engineering Department, Rasht Branch, Islamic Azad University, Rasht, Iran.
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