Objectives: Diabetic foot ulcer (DFU) is a critical complication of diabetes that can lead to severe outcomes such as infection, amputation, and increased mortality if left untreated. Early detection and continuous monitoring are essential but remain challenging, especially in resource-limited settings such as India. This study developed and validated a deep learning algorithm to classify diabetic foot images into severity grades based on the International Working Group on the Diabetic Foot classification: grade 0 (healthy), grade 1 (mild), grade 2 (moderate), and grade 3 (severe). Methods: A dataset of 407 clinical images was collected from open-source platforms and clinics in South India and expanded to 612 images through data augmentation. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Multiple machine learning models were tested, including MobileNetV2, EfficientNet-b0, DenseNet121, ResNet₅0, VGG16, and ViTb₁6. Results: Among the evaluated models, MobileNetV2 demonstrated the highest validation accuracy (82%) and achieved an F1-score of 79% on the test set. Although the model showed strong training accuracy, minor overfitting was observed, particularly in distinguishing adjacent severity grades. To address this, dropout, batch normalization, and early stopping were employed. Overall, the model generalized well, showing high accuracy in detecting healthy cases and acceptable performance across ulcer severity grades. Conclusions: This study underscores the potential of machine learning-based tools to support frontline healthcare workers and facilitate patient self-monitoring in low-resource environments. Future work will focus on refining the model and integrating it into user-friendly applications.
Satheesh et al. (Sat,) studied this question.