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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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آمار تعداد مشاهده مقاله: 573 تعداد دریافت فایل اصل مقاله: 452 |
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