Automated Diabetic Foot Ulcer Detection and Classification Using Deep Learning

Diabetic foot ulcers (DFU) are a common and serious complication in individuals with diabetes, and early detection plays a crucial role in effective treatment and prevention of further complications. Automated DFU Detection and Classification using Deep learning (DL) refers to the application of deep learning techniques to automatically detect and classify diabetic foot ulcers from medical images. DL, a subfield of machine learning, has shown promising results in medical imaging analysis, including diabetic foot ulcer detection. The use of deep learning in DFU detection provides various benefits, including the ability to learn complex features, adaptability to different image modalities, and the potential for high accuracy in detection and classification tasks. Therefore, this article introduces a novel sparrow search optimization (SSO) with deep learning enabled diabetic foot ulcer detection and classification (SSODL-DFUDC) technique. The presented SSODL-DFUDC technique's goal lies in identifying and classifying DFU. The proposed technique employs the Inception-ResNet-v2 model for feature vector generation to accomplish this. Since the trial and error manual hyperparameter tuning of the Inception-ResNet-v2 model is a tedious and erroneous process, the SSO algorithm can be used for the optimal hyperparameter selection of the Inception-ResNet-v2 model which in turn enhances the overall DFU classification results. Moreover, the classification of DFU takes place using the stacked sparse autoencoder (SSAE) model. The comprehensive experimental outcomes demonstrate the improved performance of the SSODL-DFUDC system related to existing DL techniques.