CNN-SVM-Based Tinea Unguium Classification Approach to Telemedicine System

Abstract

In recent years, the number of patients with diabetes mellitus has increased. Moreover, the prevalence of diabetes among older age groups in Japan is notably higher, indicating a growing proportion of patients with diabetes. Diabetic foot complications manifests in some patients with diabetes mellitus; hence, timely identification of the symptoms associated with diabetic feet is crucial for preventing severe complications. It is imperative for patients to observe their feet regularly; however, recognizing diabetic foot symptoms can be challenging for individuals without medical expertise owing to the variability of such symptoms. In this study, we focused on tinea unguium as a case type and utilized machine learning to classify images of tinea unguium and normal feet. The evaluation results showed that the combination of ResNet-50 and a support vector machine yielded the best performance when applied to a dataset of the acquired images of the nail regions the feet.