Clinical Assessment of Diabetic Foot Ulcers Using GWO-CNN based Hyperspectral Image Processing Approach

ABSTRACT

Diabetes Mellitus has turned out to be a complicated disease and as of 2016 one out of eleven humans suffer from this disease leading to Diabetic Foot Ulcers (DFU). When not treated, DFUs lead to amputation and in this work, a novel image processing method is proposed for the efficient assessment and classification of DFU images. Initially, pre-processing is done by cascaded fuzzy filter followed by nonlinear partial differential equation (NPDE) based segmentation that segments the foot ulcer regions. Consequently, the local binary pattern (LBP) is employed to extract the useful features. Then the proposed hybrid Grey Wolf Optimization-Convolutional Neural Network (GWO-CNN) model uses these features to identify the DFU regions. The performance evaluation is done by the estimation of the performance metrics and the results are compared with existing algorithms indicating the efficacy of the proposed technique. The obtained results reveal that the proposed work generates an accuracy of 98.5% with a reduced error percentage of 1.4%.

KEYWORDS:

• Classification
• CNN
• Diabetic foot
• Diabetic foot ulcer
• GWO
• LBP
• NPDE

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

T. Arumuga Maria Devi

T Arumuga Maria Devi received BE degree in electronics & communication engineering from Manonmaniam Sundaranar University, Tirunelveli India in 2003, MTech degree in computer & information technology from Manonmaniam Sundaranar University, Tirunelveli, India in 2005 and worked as lecturer in the Department of Electronics & Communication Engineering in Sardar Raja College of Engineering and also received PhD degree in information technology, computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli, India in 2012. She is the assistant professor at the Centre for Information Technology and Engineering of Manonmaniam Sundaranar University from November 2005 onwards. Her research interests include signal and image processing, multimedia, and remote communication.

R. Hepzibai

R Hepzibai received an MSc degree in software engineering from Periyar University, Salem, Tamil Nadu, India, in May 2006. Currently, she is doing a PhD in computer and information technology, at the Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Her area of research includes hyperspectral image processing, machine learning, and the internet of things (IOT). Email: [email protected]