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Almusallam, Naif, Muradova, Vusala, Abotaleb, Mostafa, Makarovskikh, Tatiana, Alkattan, Hussein, Gamal Ahmed, Omar, Mohsin Mijwil, Maad. (1405). A U-Net framework using differential equations for enhanced computer vision in lung disease diagnosis. سامانه مدیریت نشریات علمی, 14(1), 374-391. doi: 10.22034/cmde.2024.64290.2905
Naif Almusallam; Vusala Muradova; Mostafa O Abotaleb; Tatiana Makarovskikh; Hussein Alkattan; Omar Gamal Ahmed; Maad Mohsin Mijwil. "A U-Net framework using differential equations for enhanced computer vision in lung disease diagnosis". سامانه مدیریت نشریات علمی, 14, 1, 1405, 374-391. doi: 10.22034/cmde.2024.64290.2905
Almusallam, Naif, Muradova, Vusala, Abotaleb, Mostafa, Makarovskikh, Tatiana, Alkattan, Hussein, Gamal Ahmed, Omar, Mohsin Mijwil, Maad. (1405). 'A U-Net framework using differential equations for enhanced computer vision in lung disease diagnosis', سامانه مدیریت نشریات علمی, 14(1), pp. 374-391. doi: 10.22034/cmde.2024.64290.2905
Almusallam, Naif, Muradova, Vusala, Abotaleb, Mostafa, Makarovskikh, Tatiana, Alkattan, Hussein, Gamal Ahmed, Omar, Mohsin Mijwil, Maad. A U-Net framework using differential equations for enhanced computer vision in lung disease diagnosis. سامانه مدیریت نشریات علمی, 1405; 14(1): 374-391. doi: 10.22034/cmde.2024.64290.2905

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

Computational Methods for Differential Equations
مقاله 26، دوره 14، شماره 1، فروردین 2026، صفحه 374-391    اصل مقاله (3.33 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2024.64290.2905
نویسندگان
Naif Almusallam1؛ Vusala Muradova2؛ Mostafa O Abotaleb* 3؛ Tatiana Makarovskikh4؛ Hussein Alkattan4؛ Omar Gamal Ahmed5؛ Maad Mohsin Mijwil6
1Department of Management Information Systems (MIS)‎, ‎School of Business‎, ‎King Faisal University (KFU)‎, ‎Al-Ahsa 31982‎, ‎Saudi Arabia.
2Lankaran State University‎, ‎Lankaran‎, ‎Azerbaijan.
3Engineering School of Digital Technologies, Yugra State University, Khanty Mansiysk, 628012, Russia.
4Department of System Programming‎, ‎South Ural State University‎, ‎Chelyabinsk‎, ‎454080‎, ‎Russia.
5Department of Electric Drive‎, ‎Mechatronics and Electromechanics‎, ‎South Ural State University‎, ‎Chelyabinsk‎, ‎454080‎, ‎Russia.
6College of Administration and Economics, Al-Iraqia University, Baghdad, Iraq.
چکیده
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.
کلیدواژه‌ها
U-Net؛ Lung disease classification؛ Chest X-ray؛ Deep learning؛ Medical image analysis؛ Convolutional neural networks
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