Segmentation of Multiple Sclerosis Lesions in MRI Using U‐Net With a Wavelet Transformer‐Based Attention Mechanism

ABSTRACT Multiple sclerosis (MS) is a chronic autoimmune central nervous system disorder characterized by the immune system's mistaken attack on myelin, leading to disrupted neural signal transmission and diverse neurological impairments. Early and accurate diagnosis is crucial for effective disease management. While lesion detection in MRI scans plays a fundamental role in diagnosis, the small size and scattered distribution of lesions present significant challenges. Deep learning‐based automatic segmentation has shown promise in improving diagnostic accuracy. U‐Net is a widely adopted architecture for medical image segmentation, yet its dense decoder layers hinder high‐resolution feature extraction. This study introduces an enhanced U‐Net architecture incorporating a local attention mechanism and wavelet transform within skip connections, complemented by a spatial attention mechanism in the bottleneck. These modifications enable the model to capture both local and global image features, leading to more precise lesion segmentation. The proposed method was evaluated on the ISIB2015 dataset, demonstrating superior performance compared to conventional U‐Net and similar approaches in terms of dice similarity coefficient (DSC) and positive predictive value (PPV).