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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    Titel :

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


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Abdul Majeed, Anwar P.P. (Herausgeber:in) / Yap, Eng Hwa (Herausgeber:in) / Liu, Pengcheng (Herausgeber:in) / Huang, Xiaowei (Herausgeber:in) / Nguyen, Anh (Herausgeber:in) / Chen, Wei (Herausgeber:in) / Kim, Ue-Hwan (Herausgeber:in) / Xian, Qixin (Autor:in) / Chibeze, Precious (Autor:in) / Li, Guojie (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    29.11.2024


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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