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Enhancing the Diagnosis of Skin Neglected Tropical Diseases by Artificial Neural Networks Using Evolutionary Algorithms: Implementation on Raspberry Pi


Neglected TROPICAL diseases related to the skin with similar manifestations in their early phase persist in remote areas and are characterized by the prevailing poverty of populations. If not detected early, they very often lead to severe ulcerations and permanent disabilities. We present an approach to optimize the early detection of neglected tropical skin diseases (NTDs) by automatic identification of skin lesions. As contributions, we propose a web-mobile AI-powered automatic skin lesion recognition system optimized by a new hybrid Whale-Shark optimization algorithm (WOA-SSO-ANN) that can help frontline health workers without state-of-the-art equipment to detect NTDs in their beginning stage. We extract the relevant regions features of the lesions. The dataset resulting from this preprocessing is classified by artificial neural networks optimized by a new hybrid Whale-Shark optimization algorithm to develop an improved artificial neural network in terms of processing time and/or accuracy. The best result was obtained with an overall classification accuracy of 93% and a processing time reduced by almost half compared to other optimizers. The proposed application is able to recognize cases of Buruli ulcer, leprosy, and leishmaniasis in our database (nodule and plaque) and classify new patients, thus reducing the cost of management of these diseases when they are detected late. The AI models implemented in this work have satisfactory accuracy and could be a complementary diagnostic tool, especially in remote areas where medical specialists are scarce.

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