A U-Net framework using differential equations for enhanced computer vision in lung disease diagnosis
نویسندگان
1 Department of Management Information Systems (MIS), School of Business, King Faisal University (KFU), Al-Ahsa 31982, Saudi Arabia.
2 Engineering School of Digital Technologies, Yugra State University, Khanty Mansiysk, 628012, Russia.
3 Department of System Programming, South Ural State University, Chelyabinsk, 454080, Russia.
4 Department of Electric Drive, Mechatronics and Electromechanics, South Ural State University, Chelyabinsk, 454080, Russia.
5 Lankaran State University, Lankaran, Azerbaijan.
6 Department of System Programming, South Ural State University, Chelyabinsk, 454080, Russia.
7 College of Administration and Economics, Al-Iraqia University, Baghdad, Iraq.
doi
10.22034/cmde.2024.64290.2905چکیده
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.
