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박지원(Jiwon Park),이세형(Seihyoung Lee),반윤지(Yun-Ji Ban),민기현(Gihyeon Min),김정은(Jeongeun Kim) 한국정보기술학회 2024 한국정보기술학회논문지 Vol.22 No.2
A classification model using deep learning was developed for automatic diagnosis of diabetic foot ulcer disease. In this study, we implemented an automated medical image disease diagnosis model that can classify diabetic foot ulcers into normal and diseased, and classify them into infection and ischemia, complex ulcers, and other ulcers by using a publicly available image dataset of diabetic foot ulcers(DFU challenge 2021). For this purpose, six deep neural network models were used to train, and the accuracy, recall, precision, and f1-score performance of the models were compared and evaluated for each model to present a model with performance suitable for diabetic foot ulcer classification. To improve the performance of model, data augmentation and imbalanced data processing were used, and among them, the EfficientNetB3 model achieved 89% accuracy and 89% recall for diabetic foot ulcer data.