Keywords
Abstract
The early and accurate detection of pulmonary diseases is critical for improving patient outcomes and optimizing treatment strategies. Recent advancements in deep learning (DL) and artificial intelligence (AI) have enabled the development of automated models capable of analyzing medical imaging data, including CT and MRI scans, with high precision. This paper presents an overview of deep learning approaches for detecting various pulmonary pathologies, including pneumonia, chronic obstructive pulmonary disease (COPD), and lung cancer, in CT and MRI images. The study discusses convolutional neural networks (CNNs), transfer learning, and hybrid models, emphasizing their accuracy, sensitivity, and clinical applicability. Challenges such as limited annotated datasets, variability in imaging quality, and integration into clinical workflows are also addressed. By highlighting current advancements and potential improvements, this paper underscores the transformative role of deep learning in enhancing diagnostic efficiency and supporting radiologists in clinical decision-making.
References
1. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 35(5), 1207–1216. https://doi.org/10.1109/TMI.2016.2525805
2. Ardila, D., Kiraly, A. P., Bharadwaj, S., et al. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest CT. Nature Medicine, 25(6), 954–961. https://doi.org/10.1038/s41591-019-0447-x
3. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
4. Shen, W., Zhou, M., Yang, F., et al. (2017). Multi-scale convolutional neural networks for lung nodule classification. Information Processing in Medical Imaging, 10265, 588–599. https://doi.org/10.1007/978-3-319-59050-9_46
5. Wang, S., Su, Z., Ying, L., et al. (2016). Accelerating magnetic resonance imaging via deep learning. IEEE Transactions on Medical Imaging, 35(5), 1304–1315. https://doi.org/10.1109/TMI.2016.2531538
6. Rajpurkar, P., Irvin, J., Ball, R. L., et al. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686
7. Базарбаев, М. И., & Сайфуллаева, Д. И. (2022). РахиǦ мов Б Т., Ж˲ раева З Р. Роль информационных техǦ нологий в медицине и биомедицинской инженеǦ рии в подготовке будущих специалистов в пеǦ риод цифровой трансформации в образовании. ТТА Ахборотномаси, 10(10), 8Ǧ13.
8. Марасулов, А. Ф., Базарбаев, М. И., Сайфуллаева, Д. И., & Сафаров, У. К. (2018). Подход к обучению математике, информатике, информационным технологиям и их интеграции в медицинских вузах.
9. Baxtiyorovna, E. D., Alisherovna, F. N., & Jurayeva, U. O. N. (2024). PROPERTIES OF ELECTRON AND NEUTRON THERAPY. Web of Medicine: Journal of Medicine, Practice and Nursing, 2(10), 137-141.
10. Fayziyeva, N. A. (2025). OLIY TA’LIMDA PEDAGOGIK TA’LIM-TARBIYANI TASHKIL ETISHNING AHAMIYATLARI VA ZAMONAVIY METODLARIDAN FOYDALANISH USULLARI.
11. Fayziyeva, N. (2025). THE EFFECT OF MAGNESIUM ON PREGNANT WOMEN. Web of Medicine: Journal of Medicine, Practice and Nursing, 3(5), 60-63.
12. Zhang, J., Xie, Y., Wu, Q., & Xia, Y. (2020). Medical image classification using convolutional neural networks: A review. Journal of Healthcare Engineering, 2020, 8852273. https://doi.org/10.1155/2020/8852273