Automated Analysis Of Diabetic Vasculopathy Using Semantic Segmentation Of Thermal Images Of Peroneal Vessel

Introduction: Diabetic vascular disease is one of most serious health problems in diabetic patients, it causes the development of severe complications including delayed wound healing and increased susceptibility to infections. Methods: To provide accurate and are non-invasive diagnosis, current work emphasizes on Diabetic Vasculopathy (DV) that is analysed with thermoregulation images through Semantic Segmentation (SS). A novel methodology was adapted, combining thermoregulation imaging with SS using the U-Net++ model to investigate temperature distributions at the skin level. This work introduces a novel method that utilizes MobileNetV2 as the encoder for fast Feature Extraction (FE). Results: The results from the suggested model, achieves a segmentation accuracy of 95%, which is significantly more compared to that of DeepLabV3+ and PSPNet models. A mean and Intersection over Union (IoU) of 85% and 87% was reported by the suggested frameworks throughout the training and validation phases. Conclusion: Classifying normal and abnormal regions can be done via the outcomes, as it offers the great visibility in the thermal image for clinicians by detecting the non-thermal regions