MS Identification in Brain Magnetic Resonance Images Using Wavelet Transfer Learning
Publish place: Journal of Computer and Robotics، Vol: 13، Issue: 2
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 173
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JCR-13-2_002
تاریخ نمایه سازی: 18 مهر 1400
Abstract:
Multiple Sclerosis (MS) is one of the most important diseases of the central nervous system. This disease causes small lesions detectable in Magnetic Resonance Imaging (MRI) images of the patient’s brain. Because of the small size of the lesions, their distribution, and their similarity to some other diseases, the MS diagnosis can be difficult for specialists and may be mistaken. In this paper, we presented a new method based on deep learning for the automatic classification of MRI images. The proposed method is a combinational architecture from transfer learning and wavelet transform (WT). First, WT was applied to the input MRI image, and its four output sub-bands are used as the input of four fine-tuning networks based on EfficientNet-B۳. Transfer learning networks perform feature extraction on all four sub-bands. Then, their outputs are combined, and the result is classified by a fully connected neural network. Due to the feature of WT to extract local features, it was possible to highlight the lesions in the images and subsequently classify it with higher accuracy and precision. Various criteria have been used to evaluate the proposed method. The results of the experiments show that the Values of accuracy, precision, sensitivity, and specificity are ۹۸.۹۱%, ۹۹.۲۰%, ۹۹.۲۰%, and ۹۸.۳۳%, respectively.
Keywords:
multiple sclerosis (MS) , deep learning , transfer learning , Wavelet , Magnetic Resonance Imaging (MRI)
Authors
Ali Alijamaat
Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
Ali NikravanShalmani
Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Peyman Bayat
Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran