Implementation Diabetic Foot Ulcer Types Detection using Neural Networks

Computer Science

Diabetic Foot Ulcer Infection is one of the essential causes that reduces quantity and degrades quality of the Medical Diagnosis merchandises. Diabetic Foot Ulcer Infections have turned into a terrible as it can cause significant reduction in both quality and quantity of Medical Diagnosis products. Images form important data and information in biological sciences. Until recently photography was the only method to reproduce and report such data. It is difficult to quantify or treat the photographic data mathematically. This project, classifies the Diabetic Foot Ulcer at hand into infected and non-infected classes. The developing software provides a fast and accurate method in which the Diabetic foot Ulcer Infections are detected and classified using k-means based segmentation and neural networks-based classification. Most common Infections seen in the foot of Tapioca and Mango are discussed here for this approach. In this paper, respectively, the applications of K-means clustering and Neural Networks (NNs) have been formulated for clustering and classification of Infections that effect on Diabetic Foot Ulcer foot. Recognizing the Infection is mainly the purpose of the proposed approach. Thus, the proposed Algorithm was tested on three types of diabetic foot ulcer described namely neuropathic, neuroischaemic, and ischaemic. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of Diabetic foot Ulcer Infections in a little computational effort. This project gives 95% of efficiency using MATLAB simulation results.

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