نوع مقاله : علمی-پژوهشی
نویسندگان
1 دانشگاه یزد دانشکده مهندسی کامپیوتر
2 هیات علمی / دانشگاه یزد
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In recent years, medical imaging has made significant advancements with the use of deep learning techniques. Multiple sclerosis (MS) is a chronic disease caused by the demyelination of the central nervous system. This disease is characterized by plaques visible in MRI scans. Accurate detection of these plaques is crucial for prognosis and treatment monitoring. Manual segmentation of MS plaques by experts is a time-consuming process and prone to human error. This study presents a transformer-based approach for MS plaque segmentation. The proposed model consists of a swin transformer-based encoder for feature extraction and a specialized decoder for segmentation map reconstruction. The output is a labeled mask of the segmented plaques. The model was evaluated on ISBI2015 dataset, and its performance was compared with the classical U-Net model. The results show that the proposed model improved IoU and Dice scores to 0.71 and 0.83, respectively, outperforming U-Net model, which achieves IoU and Dice scores of 0.64 and 0.78.
کلیدواژهها [English]
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