A U-Net framework using differential equations for enhanced computer vision in lung disease diagnosis

Document Type : Research Paper

Authors

1 Department of Management Information Systems (MIS)‎, ‎School of Business‎, ‎King Faisal University (KFU)‎, ‎Al-Ahsa 31982‎, ‎Saudi Arabia.

2 Lankaran State University‎, ‎Lankaran‎, ‎Azerbaijan.

3 Engineering School of Digital Technologies, Yugra State University, Khanty Mansiysk, 628012, Russia.

4 Department of System Programming‎, ‎South Ural State University‎, ‎Chelyabinsk‎, ‎454080‎, ‎Russia.

5 Department of Electric Drive‎, ‎Mechatronics and Electromechanics‎, ‎South Ural State University‎, ‎Chelyabinsk‎, ‎454080‎, ‎Russia.

6 College of Administration and Economics, Al-Iraqia University, Baghdad, Iraq.

Abstract

This study presents a U-Net-based approach for the classification of lung diseases using chest X-ray images. The model effectively leverages its encoder-decoder architecture and skip connections to capture both high-level semantic features and detailed spatial information, crucial for medical image analysis. The U-Net model was trained and tested on a dataset of 3,475 X-ray images, representing three classes: Normal, Lung Opacity, and Viral Pneumonia. The model achieved strong performance, with a weighted F1 score of 0.9770 and Cohen’s Kappa of 0.9653, demonstrating its high accuracy in classifying lung diseases. These results confirm the suitability of U-Net for medical imaging tasks, particularly in detecting subtle abnormalities in chest X-ray images. However, the study also identifies challenges, including class imbalance in medical datasets and the computational demands of training large models like the U-Net. Future improvements could focus on enhancing generalizability and reducing computational complexity through advanced data augmentation, domain adaptation, and architectural optimizations. Overall, this research highlights the potential of U-Net for developing reliable and efficient automated diagnostic tools in healthcare.

Keywords

Main Subjects

References

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Computational Methods for Differential Equations
Volume 14, Issue 1
January 2026
Pages 374-391

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History

  • Receive Date: 31 October 2024
  • Revise Date: 26 November 2024
  • Accept Date: 14 November 2024

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