Diabetic foot ulcers (DFUs) can lead to severe infections and amputations if not detected early. This study aims to identify and extract features from DFU datasets using a class of deep learning approach known as feature-based transfer learning for improved diagnosis. The dataset contains normal and abnormal feet images with existing ulcers. A pre-trained convolutional neural network model, viz. VGG19 is utilized to extract discriminative features from the images. Two supervised learning methods, namely k-Nearest Neighbours (kNN) and Logistic Regression (LR), are then trained on these images to classify them as normal or abnormal. The data is split into 70:15:15 train, validation and test sets. It was illustrated from the study that the VGG19 + LR pipeline is able to distinguish the classes of DFUs well, suggesting its promising potential for computer-aided diagnosis.
Computer-Aided Diagnosis of Diabetic Foot Ulcers: A Feature-Based Transfer Learning Investigation
Lect. Notes in Networks, Syst.
International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023
29.11.2024
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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