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Application of Machine Learning Techniques to Solve the Problem of Skin Diseases Diagnosis

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Reliability Engineering and Computational Intelligence for Complex Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 496))

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Abstract

Solving the problem of remote diagnosis of diseases, including the use of telemedicine, is an urgent task. When diagnosing dermatological diseases, the input information is an image of a skin area with a certain skin lesion. Currently, machine learning is widely used in medicine, and, as a rule, machine learning methods solve the task of recognition and classification depending on the subject area. The problem of the research is the selection of the optimal algorithm or, as it is also called, the filter, which will increase the quality of the image so that the neural network can clearly understand the disease area and recognize it. In this study, Sobel methods, the method of principal components, and brightness normalization are used to improve image quality. After each processing, the data is fed to a convolutional neural network based on the TensorFlow framework. The developed neural network is used for skin diseases classification.

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Authors and Affiliations

  1. Faculty of Electronics and Information Technologies, Sumy State University, Sumy, Ukraine

    Eduard Kinshakov & Yuliia Parfenenko

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  1. Eduard Kinshakov
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  2. Yuliia Parfenenko
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Correspondence to Eduard Kinshakov .

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Editors and Affiliations

  1. Computing and Engineering, United Kingdom and TNO, Healthy Living & Work, Leiden, University of Huddersfield, Huddersfield, The Netherlands

    Coen van Gulijk

  2. Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia

    Elena Zaitseva

  3. Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia

    Miroslav Kvassay

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Kinshakov, E., Parfenenko, Y. (2023). Application of Machine Learning Techniques to Solve the Problem of Skin Diseases Diagnosis. In: van Gulijk, C., Zaitseva, E., Kvassay, M. (eds) Reliability Engineering and Computational Intelligence for Complex Systems. Studies in Systems, Decision and Control, vol 496. Springer, Cham. https://doi.org/10.1007/978-3-031-40997-4_7

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