Automated Detection of Diabetic Foot Using Thermal Images by Neural Network Classifiers
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2017, Vol 4, Issue 5
Abstract
Diabetes is one of the major problems worldwide. It is a metabolic disease where the improper management of blood glucose level lead to the risk of many diseases like foot complications, amputation, kidney diseases etc. Early detection of diabetes and their precursors is essential in preventing their devastating consequences such as foot infection and amputation. There are various methods to diagnose diabetes which are invasive techniques. Infrared thermography is a promising modality for such a system from which diabetes is noninvasively detected using the thermal foot images. The temperature differences between corresponding areas on feet are the clinically significant parameters. The thermal images are preprocessed, segmented using kmeans clustering and then the textural features (GLCM) are extracted and then classified using classifiers. In this paper, to diagnose diabetic foot, three models like Probabilistic Neural Network(PNN), K-nearest Neighbor Network(KNN), and Support vector machine(SVM) are described and their performances are compared. Experimental results show that KNN has an accuracy of 95.66 %. This infers that KNN model outperforms the other two models
Authors and Affiliations
S. Purnima
Keywords
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