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|Title:||Automated detection of diabetic foot with and without neuropathy using double density-dual tree-complex wavelet transform on foot thermograms||Authors:||Muhammad Adam
Ng, Eddie Yin Kwee
Oh, Shu Lih
Heng, Marabelle L.
Tan, Jen Hong
Tong, Jasper W. K.
Acharya, U. Rajendra
|Keywords:||Engineering::Mechanical engineering||Issue Date:||2018||Source:||Muhammad Adam, Ng, E. Y. K., Oh, S. L., Heng, M. L., Hagiwara, Y., Tan, J. H., . . . Acharya, U. R. (2018). Automated detection of diabetic foot with and without neuropathy using double density-dual tree-complex wavelet transform on foot thermograms. Infrared Physics and Technology, 92, 270-279. doi:10.1016/j.infrared.2018.06.010||Journal:||Infrared Physics and Technology||Abstract:||Diabetic foot is the most common problem among diabetic patients, mainly due to peripheral vascular and neuropathy induced capillary perfusion changes. These pathogenic factors cause superficial temperature changes that can be qualitatively and visually documented using infrared thermography (IRT). Hence, IRT can potentially be used to evaluate the diabetic foot. However, it is tedious to manually interpret these subtle temperature variations by inspecting the feet thermal image. Therefore, an automated system to detect diabetic foot with and without neuropathy is proposed. In this study, 51 healthy individuals and 66 diabetic patients (33 with and 33 without neuropathy) are considered. The segmented plantar foot thermograms are decomposed into coefficients using double density-dual tree-complex wavelet transform (DD-DT-CWT). Several entropy and texture features are extracted from the decomposed images of left, right and bilateral foot. These features are reduced using various dimensionality reduction techniques and subsequently ranked using F-values. The ranked features are fed individually into the different classifiers one by one. The developed system yielded 93.16% accuracy, 90.91% sensitivity and 98.04% specificity using only four locality sensitive discriminant analysis (LSDA) features obtained from bilateral foot thermal images with k-nearest neighbour (kNN) classifier. This automated diabetic foot detection system can be introduced in polyclinics and hospitals to clinically support the clinicians to confirm their manual diabetic foot diagnosis.||URI:||https://hdl.handle.net/10356/136849||ISSN:||1350-4495||DOI:||10.1016/j.infrared.2018.06.010||Rights:||© 2018 Elsevier B.V. All rights reserved. This paper was published in Infrared Physics and Technology and is made available with permission of Elsevier B.V.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Journal Articles|
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