DEVELOPMENT OF ALGORITHMS FOR DETECTION AND CLASSIFICATION OF LUNG DISEASES BASED ON X-RAY IMAGES USING ARTIFICIAL INTELLIGENCE DEEP LEARNING MODELS
Received: 2026-05-20
Published: 2026-06-06
Abstract
The article examines the development of intelligent algorithms for automatic detection and classification of lung diseases based on chest X-ray images. Modern deep learning approaches for diagnosing pneumonia, tuberculosis, COVID-19, and lung cancer are analyzed. A system architecture including image preprocessing, model training, performance evaluation, and result interpretation is proposed. The study demonstrates that artificial intelligence models can significantly improve diagnostic accuracy and reduce the workload of radiologists.
Keywords
List of references
-
World Health Organization. Global Health Estimates 2024.
-
Felson's Principles of Chest Roentgenology. 5th ed. Philadelphia: Elsevier; 2020.
-
Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press; 2016.
-
Wang X., et al. ChestX-ray8: Hospital-scale chest X-ray database and benchmarks. CVPR; 2017.
-
Shorten C., Khoshgoftaar T. A survey on image data augmentation. J Big Data. 2019;6:60.
-
Pan S.J., Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–1359.
-
Selvaraju R.R., et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. ICCV; 2017.
-
Pranav Rajpurkar, et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays. Stanford University; 2017.
About the Authors
License
This work is licensed under a Creative Commons Attribution 4.0 International License.