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.


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    Title :

    Computer-Aided Diagnosis of Diabetic Foot Ulcers: A Feature-Based Transfer Learning Investigation


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:

    Conference:

    International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023



    Publication date :

    2024-11-29


    Size :

    6 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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