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.

中文翻译：

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基于小波卷积神经网络的脑部MRI图像多发性硬化症识别

多发性硬化症（MS）是中枢神经系统中神经周围覆盖物的变性疾病。它会破坏免疫细胞，并在患者的大脑中造成小的损伤。可以采用自动图像识别技术来提高检测的准确性。卷积神经网络（CNN）的使用是检测图像中病变的最常见的深度学习方法。由于MS病变的特定特征，使用光谱特征（尤其是多分辨率）可以突出显示图像病变并导致更准确的诊断。在本研究中，应用Haar小波变换来利用光谱信息。所提出的方法是二维离散Haar小波变换和CNN网络的组合。