Classification of diabetic foot ulcer using convolutional neural network (CNN) in diabetic patients

Abstract

The image of chronic wounds on human skin tissue has the similar look in shape, color and size to each other even though they are caused by different diseases. Diabetic ulcer is a condition where peripheral arterial blood vessels are disrupted due to hyperglycemia in people with diabetes mellitus. This research was aimed to analyze the accuracy of the Convolutional Neural Network algorithm in classifying diabetic ulcer disease with a transfer learning approach based on the appearance of the image of the wound on the sole in people with diabetes mellitus. By applying the transfer learning approach, the results showed that the Resnet152V2 model achieved the best accuracy value of 0.993 (99%), precision of 1.00, recall of 0.986, F1-Score of 0.993 and Support of 72. Therefore, the ResNet152V2 model was highly considered for classifying diabetic ulcer in patients with diabetes melitus.

Diyabetik Ayağın Derin Öğrenme Yöntemleriyle Ayırıcı Tanısı

“…This approach, DFU SPNet, can assist DFU specialists in expediting their decision-making process. Harahap et al (2022), evaluated the CNN algorithm, ResNet152V2, for its precision in categorizing diabetic ulcer disease using transfer learning methodology. The ResNet152V2 model achieved the highest accuracy score of 0.993, recall score of 0.986, precision score of 1.00, F1-Score of 0.993, and support score of 72.…”