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Multiple sclerosis identification in brain MRI images using wavelet convolutional neural networks
Author(s) -
Alijamaat Ali,
NikravanShalmani Alireza,
Bayat Peyman
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22492
Subject(s) - convolutional neural network , artificial intelligence , haar wavelet , pattern recognition (psychology) , computer science , haar , wavelet transform , multiple sclerosis , wavelet , discrete wavelet transform , image (mathematics) , computer vision , medicine , psychiatry
Multiple sclerosis (MS) is a degenerative disease of the covering around the nerves in the central nervous system. It damages the immune cells and causes small lesions in the patient's brain. Automated image recognition techniques can be employed for increasing the accuracy of detection. The use of convolutional neural networks (CNN) is the most common deep learning method for detecting lesions in image. Due to the specific features of MS lesions, the use of spectral features especially multiresolution enables the highlighting of images lesions and leads to a more accurate diagnosis. In the present study, the Haar wavelet transform was applied to make use of the spectral information. The proposed method is a combination of the two‐dimensional discrete Haar wavelet transform and the CNN network. Experiments on the image data of 38 patients and 20 healthy individuals revealed accuracy, precision, and sensitivity of 99.05%, 98.43%, and 99.14%, respectively.