Devising a comprehensive approach to diagnosing breast cancer subtypes automatically based on deep neural networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.344041

Keywords:

Attention U-Net, genetic algorithm, neural network architecture optimization, IHC images, biomedical image segmentation, automatic diagnosing of breast cancer

Abstract

This study investigates the process of analyzing immunohistochemical images of breast cancer. The study has contributed to solving the task of a standardized and objective approach to the quantitative assessment of immunohistochemical biomarkers, which would minimize inter-individual variability in assessments and could be computationally efficient for the analysis of biomedical images.

This paper aims to balance model complexity and generalization by using evolutionary algorithms to tune deep neural networks for biomedical tasks, analyzing how network structure affects performance.

Experiments were conducted on the segmentation of immunohistochemical images on 13 different architectures of neural networks. The evaluation was performed using five accuracy metrics, which allowed for an objective comparison of model performance. The use of a genetic algorithm to optimize the neural network architecture made it possible to adaptively find combinations of parameters, in particular the number of layers and the size of the base filter. The evolutionary approach enabled effective exploration of configuration space, which led to an increase in the Dice metric to 0.74. The resulting increase in accuracy indicates the model’s improved ability to segment images with different characteristics, demonstrating the practical effectiveness of the proposed approach for biomedical diagnosis tasks.

The optimized architecture was used to design a system for diagnosing breast cancer automatically based on neural networks, in particular for the method of automatic diagnosis of breast cancer subtypes. That contributed to improving the accuracy of biomedical image analysis, which could help improve the diagnostic process in clinical practice

Author Biographies

Oleh Berezsky, West Ukrainian National University

Doctor of Technical Sciences

Department of Computer Engineering

Pavlo Liashchynskyi, West Ukrainian National University

Lecturer, PhD Student

Department of Computer Engineering

Petro Liashchynskyi, Lviv Polythechnic National University

Doctor of Philosophy (PhD)

Department of Automated Control Systems

Petro Selskyy, I. Horbachevsky Ternopil National Medical University of the Ministry of Health of Ukraine

Doctor of Medical Sciences

Department of Pathologic Anatomy, Autopsy Course and Forensic Pathology

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Devising a comprehensive approach to diagnosing breast cancer subtypes automatically based on deep neural networks

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Published

2025-12-31

How to Cite

Berezsky, O., Liashchynskyi, P., Liashchynskyi, P., & Selskyy, P. (2025). Devising a comprehensive approach to diagnosing breast cancer subtypes automatically based on deep neural networks. Eastern-European Journal of Enterprise Technologies, 6(2 (138), 15–25. https://doi.org/10.15587/1729-4061.2025.344041

Issue

Vol. 6 No. 2 (138) (2025): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2025 Oleh Berezsky, Pavlo Liashchynskyi, Petro Liashchynskyi, Petro Selskyy

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