Deep Learning-Based Early Detection of Diabetic Foot Wounds Using Resnet101 and TCPO2 Standards

Diabetic Foot is a common complication among patients with Diabetes Mellitus, often associated with PAD or chronic non-healing wounds. These conditions frequently lead to recurrent hospital visits and admissions, ultimately resulting in amputations or disability. This study collects various annotated acute and chronic wound images for machine learning model training. Using TCPO2 as an evaluation standard, a deep CNN is employed for wound classification. Future integration with Mask-RCNN will enable automatic wound localization and the construction of models capable of identifying wounds with hypoxia and impaired healing potential. Lower extremity wound images from 1,000 patients were collected from Mackay Memorial Hospital. Physicians defined and annotated these images based on electronic medical records, integrating lower extremity PAD and TCPO2 data. De-identified data were grouped and used to train Resnet101. Results indicate that using TCPO2 as a classification standard is feasible. Among various tested criteria, classification using TCPO2 values greater than or less than 30 showed the best performance, with an accuracy of 86.0 ± 0.8. These findings confirm that the study’s outcomes can assist healthcare professionals and patients in early detection and diagnosis of complications, facilitating timely treatment.