Potential Use of U-Net and Fuzzy Logic in Diabetic Foot Ulcer Segmentation: A Comprehensive Review - Rachman Hidayat; Annastasya Nabila Elsa Wulandari; Purwono, Purwono; Khoirun Nisa (Peneliti Teknologi Teknik Indonesia, 2024)
Diabetic foot ulcer (DFU) image segmentation is still an interesting concern of researchers. Various new deep learning-based methods have been proposed to handle this image segmentation problem. Some research problems that are still faced by many researchers are dataset problems that are considered limited and need further clinical trials. The challenges of data problems include heterogeneity and image quality variations in the shape of skin lesions and subjectivity when annotating. The evaluation results from previous studies also show a considerable difference where there are still low accuracy results, but also too high accuracy is still found so that it is considered to have the potential for overfitting. As a result of the review of various related studies, there is an interesting potential of applying fuzzy logic to the U-Net architecture. This architecture has become very popular because it is widely used in medical image segmentation. The application of fuzzy logic can be applied to the U-Net architecture such as encoder, decoder, skip connection to adjust various U-Net parameters.