DOI: https://doi.org/10.32515/2664-262X.2025.12(43).2.55-61
Experimental Analysis of Deep Neural Networks for Automated Object Classification Using MRI Images
About the Authors
Dmytro Uhryn, Professor, Doctor of Technical Sciences, Professor of the Department of Computer Science, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine, ORCID: https://orcid.org/0000-0003-4858-4511, e-mail: d.ugryn@chnu.edu.ua
Oleksandr Dorenskyi, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of Cybersecurity and Software Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-7625-9022, e-mail: dorenskyiop@kntu.kr.ua
Oleksii Iliuk, Senior System & Businness Analyst, Team Lead, Temabit LLC, Kyiv, Ukraine, ORCID: https://orcid.org/0000-0002-0904-3045, e-mail: olexiyilyukm@gmail.com
Yuriy Ushenko, Professor, Doctor of Physical and Mathematical Sciences, Head of the Department of Computer Science, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine, ORCID: https://orcid.org/0000-0003-1767-1882, e-mail: y.ushenko@chnu.edu.ua
Kateryna Shkidina, Master’s degree Student in Computer Science, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine, ORCID: https://orcid.org/0009-0001-8536-8095, e-mail: shkidina.kateryna@chnu.edu.ua
Abstract
The article presents a system analysis and comparative study of the efficiency of modern deep neural networks for the task of automated brain tumor classification based on MRI images. Three architectures were used in the study ResNet50, DenseNet121, and EfficientNet-B0, which belong to the most widely adopted models in computer vision. The experimental part is based on a medical dataset that was preprocessed using standard augmentation and normalization methods. Quantitative results showed that ResNet50 achieved an accuracy of 92%, DenseNet121 reached 74%, and EfficientNet-B0 demonstrated the highest performance with an accuracy of 97%. Additional qualitative metrics supported these findings: the F1-score for EfficientNet-B0 reached 0.96, indicating a strong balance between precision and recall, while ResNet50 achieved an F1-score of 0.91, and DenseNet121 scored 0.73. Visualization of classification results showed that all models correctly identified the tumor class, but their confidence levels varied from 0.74 in DenseNet121 to 0.97 in EfficientNet-B0. The qualitative analysis confirmed the suitability of EfficientNet-B0 for cases where fast and accurate inference is prioritized under limited computational resources. Despite higher computational costs, ResNet50 can be effective in tasks that require robustness and maximum precision. The obtained results highlight the significant potential of deep learning models in medical diagnostics and the development of intelligent decision-support systems in neuroradiology.
Keywords
AI, machine learning, convolutional neural networks, transfer learning, deep learning, image classification, medical diagnostics
Experimental Analysis of Deep Neural Networks for Automated Object Classification Using MRI Images
About the Authors
Dmytro Uhryn, Professor, Doctor of Technical Sciences, Professor of the Department of Computer Science, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine, ORCID: https://orcid.org/0000-0003-4858-4511, e-mail: d.ugryn@chnu.edu.ua
Oleksandr Dorenskyi, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of Cybersecurity and Software Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-7625-9022, e-mail: dorenskyiop@kntu.kr.ua
Oleksii Iliuk, Senior System & Businness Analyst, Team Lead, Temabit LLC, Kyiv, Ukraine, ORCID: https://orcid.org/0000-0002-0904-3045, e-mail: olexiyilyukm@gmail.com
Yuriy Ushenko, Professor, Doctor of Physical and Mathematical Sciences, Head of the Department of Computer Science, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine, ORCID: https://orcid.org/0000-0003-1767-1882, e-mail: y.ushenko@chnu.edu.ua
Kateryna Shkidina, Master’s degree Student in Computer Science, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine, ORCID: https://orcid.org/0009-0001-8536-8095, e-mail: shkidina.kateryna@chnu.edu.ua
Abstract
Keywords
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Citations
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Copyright (©) 2025, Dmytro Uhryn, Oleksandr Dorenskyi, Oleksii Iliuk, Yuriy Ushenko, Kateryna Shkidina