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    A machine learning model for early detection of diabetic foot using thermogram images

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    A machine learning model for early detection of diabetic foot using thermogram images.pdf (4.652Mb)
    Date
    2021-10
    Author
    Khandakar, Amith
    E.H. Chowdhury, Muhammad
    Bin Ibne Reaz, Mamun
    Hamid Md Ali, Sawal
    Hasan, Md Anwarul
    Kiranyaz, Serkan
    Rahman, Tawsifur
    Alfkey, Rashad
    Ashrif A. Bakar, Ahmad
    A. Malik, Rayaz
    ...show more authors ...show less authors
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    Abstract
    Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.
    DOI/handle
    http://dx.doi.org/10.1016/j.compbiomed.2021.104838
    http://hdl.handle.net/10576/24819
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