A machine learning model for early detection of diabetic foot using thermogram images
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التاريخ
2021-10المؤلف
Khandakar, AmithE.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
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البيانات الوصفية
عرض كامل للتسجيلةالملخص
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.
المجموعات
- الهندسة الكهربائية [2616 items ]