A Machine Learning Model for Early Detection of Diabetic Foot using
Thermogram Images
- URL: http://arxiv.org/abs/2106.14207v1
- Date: Sun, 27 Jun 2021 11:37:59 GMT
- Title: A Machine Learning Model for Early Detection of Diabetic Foot using
Thermogram Images
- Authors: Amith Khandakar, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal
Hamid Md Ali, Md Anwarul Hasan, Serkan Kiranyaz, Tawsifur Rahman, Rashad
Alfkey, Ahmad Ashrif A. Bakar, Rayaz A. Malik
- Abstract summary: Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity.
thermogram images may help to detect an increase in plantar temperature prior to DFU.
We propose a robust solution to identify the diabetic foot.
- Score: 3.8261286462270006
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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