This study explores the use of foot ultrasound technique for assessing the recognition of regions of interest (ROI’s) in the foot, so it can help streamline processes for detecting abnormal patterns, specifically in diabetic foot using RSWE elastography. It focuses on the heel’s microchamber and calcaneus zones as key ROI’s. Traditionally, ROI detection relied on conventional algorithms; however, this paper introduces a novel approach using deep learning (DL) for automatic ROI detection in heel segmentation employing YOLOv8 algorithms, refined with data from prior research. A detailed comparative analysis was conducted between single-categorical and multicategorical models to classify the heel’s critical areas. Results showed that single-categorical models achieved an average precision of 98%, recall of 88%, mAP50 of 93%, and mAP50–95 of 65%. In contrast, multi-categorical models showed a balanced performance with 90% precision and recall, 92% mAP50, and 58% mAP50–95%. These findings underscore the potential of deploying DL techniques for diabetic foot assessment, marking a significant advancement in automated and precise medical diagnostics for early diabetes detection through foot condition analysis.