抄録
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.
https://ror.org/03hv1ad10 