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

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Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1008))

Abstract

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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Author information

Authors and Affiliations

  1. Laboratory of Technology and Applied Sciences, University of Douala, Douala, Cameroon

    Steyve Nyatte, Steve Perabi, Salomé Ndjakomo Essiane & Pierre Ele

  2. Signal, Image and Systems Laboratory, University of Yaounde 1, Yaoundé, Cameroon

    Salomé Ndjakomo Essiane

  3. Laboratory of Electrical Engineering, Mechatronic and Signal Treatment, National Advanced School of Engineering, University of Yaoundé 1, Yaoundé, Cameroon

    Pierre Ele

  4. Oceanography Department, Institute of Fisheries and Aquatic Sciences, University of Douala, Douala, Cameroon

    Gregoire Abessolo

Authors
  1. Steyve Nyatte
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  2. Steve Perabi
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  3. Gregoire Abessolo
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  4. Salomé Ndjakomo Essiane
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  5. Pierre Ele
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Corresponding author

Correspondence to Steyve Nyatte .

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Editors and Affiliations

  1. Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia

    Triwiyanto Triwiyanto

  2. School of Electrical Engineering, Telkom University, Indonesia, Indonesia

    Achmad Rizal

  3. Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam

    Wahyu Caesarendra

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Nyatte, S., Perabi, S., Abessolo, G., Ndjakomo Essiane, S., Ele, P. (2023). Enhancing the Diagnosis of Skin Neglected Tropical Diseases by Artificial Neural Networks Using Evolutionary Algorithms: Implementation on Raspberry Pi. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 1008. Springer, Singapore. https://doi.org/10.1007/978-981-99-0248-4_32

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