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Computational Intelligence in Medicine

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

Two paradigms have historically formed in artificial intelligence: neurocybernetics and black box cybernetics. The cybernetics of the “black box” is based on a logical approach. The rapid development of modern medicine is due to the use of technical diagnostic tools, and the use of new information technologies. Technical means of diagnosing allow you to visualize the processes of diagnosing. Intelligent information technologies use the methods and tools of artificial intelligence and can speed up the diagnosis and improve its accuracy. The authors of the work have been working on biomedical images for many years, building CAD systems with elements of artificial intelligence. Biomedical (cytological, histological, and immunohistochemical) images are used for diagnosis in oncology. The problems of classification, generation, segmentation, and clustering of biomedical images are solved in the work. For these purposes, the following means of computational intelligence were used: CNN, GAN, and U-net. CNN is used in the paper to classify images. Several models for the classification of biomedical images are proposed and a comparative analysis with existing analogs is given. The accuracy of classification for cytological images was 86%, for histological was 84%. The authors analyzed the architectures of convolutional neural networks of the U-net type for automatic segmentation of immunohistochemical images. A modified neural network architecture for the segmentation of immunohistochemical images has been developed. Computer experiments were performed on different numbers of stages and iterations. ROC curves are built to assess the quality of segmentation of known and modified network architectures such as U-net.

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Authors and Affiliations

  1. West Ukrainian National University, Ternopil, Ukraine

    Oleh Berezsky, Oleh Pitsun, Petro Liashchynskyi, Bohdan Derysh & Natalia Batryn

Authors
  1. Oleh Berezsky
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  2. Oleh Pitsun
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  3. Petro Liashchynskyi
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  4. Bohdan Derysh
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  5. Natalia Batryn
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Corresponding author

Correspondence to Oleh Pitsun .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Berezsky, O., Pitsun, O., Liashchynskyi, P., Derysh, B., Batryn, N. (2023). Computational Intelligence in Medicine. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_28

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