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Automatic Segmentation of Lung X-Ray Images Using U-Net Convolutional Neural Network | ||
| Earth Observation and Geomatics Engineering | ||
| دوره 9، شماره 2، اسفند 2025 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2026.409772.1200 | ||
| نویسندگان | ||
| Mahdi Hasanlou* 1؛ Niloofar Darbandsari2 | ||
| 1University of Tehran | ||
| 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Objective: Medical imaging, particularly chest X-ray analysis, plays a vital role in diagnosing and treating lung diseases such as pneumonia, pulmonary fibrosis, and lung cancer. Despite its importance, accurate interpretation of these images faces several challenges, including low quality, noise, illumination variations, and the high cost and effort of manual annotation. Moreover, the complex anatomy of the lungs requires advanced algorithms to achieve precise delineation. Method: This study presents an automatic lung segmentation approach based on the U-Net convolutional neural network. With its encoder–decoder architecture, U-Net effectively integrates compressed and expanded feature representations to produce accurate segmentation. The model was trained on 563 annotated chest X-ray images and evaluated on 141 independent cases. Results: Experimental results demonstrate 91% accuracy on the training set and over 84% accuracy on the test set, confirming its strong performance in extracting lung regions. The findings reveal that U-Net can reliably detect lung structures and lesions, even with limited training data or varying image quality. This reduces dependence on manual interpretation, lowers associated costs, and minimizes human error while accelerating the diagnostic process. The model’s high generalizability further supports its potential for use across diverse clinical settings. Conclusions: In summary, this research emphasizes the value of deep learning architectures such as U-Net for precise medical image segmentation. The proposed method enhances diagnostic efficiency and accuracy, providing a reliable tool for supporting clinicians. Future extensions, including integration with complementary deep learning techniques, may further advance intelligent healthcare applications. | ||
| کلیدواژهها | ||
| Medical Image Segmentation؛ Deep Learning؛ Lung X-Ray Images؛ U-Net Model Radiological Image Processing | ||
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آمار تعداد مشاهده مقاله: 45 |
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