Optimizing Diabetic Foot Ulcer Detection: Leveraging SSD and YOLO Architectures

Diabetic foot ulcer (DFU) are a severe complication in diabetic patients, requiring accurate and early diagnosis to prevent further complications such as infection or amputation. To address this, Research employed YOLO and SSD models for automated DFU detection from medical images, comparing their performance based on key evaluation metrics, including accuracy, precision, recall, and F1-score. Results demonstrate that both models perform exceptionally well, with SSD achieving an accuracy of 98.7%, surpassing YOLO's 97.5%. Additionally, SSD exhibited superior precision (99%), recall (98.9%), and F1-score (99.1%), indicating its robustness in detecting ulcerated regions of varying sizes and stages. These results were further contextualized through a comparison with previous studies, where SSD outperformed several CNN-based models, which achieved accuracy ranging from 93.7% to 97.0%. The SSD model's multi-scale object detection capabilities, combined with transfer learning and data augmentation techniques, contributed to its high performance. Overall, this study demonstrates the potential of deploying SSD and YOLO models in clinical settings for early and accurate DFU detection, paving the way for more reliable and timely interventions in diabetic care. The findings underline the significance of advanced object detection methods, offering promising avenues for improving patient outcomes in medical imaging