02296nas a2200229   4500000000100000008004100001260003000042653003000072653003100102653002100133653003500154100001300189700001300202700001500215700002300230700001000253245015600263300001200419520159200431022002502023020001802048       2023                            d  bSpringer Nature Singapore10aArtificial neural network10aMetaheuristic optimization10aImage processing10aSkin neglect tropical diseases1 aNyatte S1 aPerabi S1 aAbessolo G1 aNdjakomo Essiane S1 aEle P00aEnhancing the Diagnosis of Skin Neglected Tropical Diseases by Artificial Neural Networks Using Evolutionary Algorithms: Implementation on Raspberry Pi  a475-4963 a<p>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.</p>
  a1876-1100, 1876-1119  a9789819902477