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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

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Abstract

In medical imaging, the last decade has witnessed a remarkable increase in the availability and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant advancement in deep learning techniques, noted for their escalating accuracy. These developments have catalyzed a surge in the application of deep learning in various medical studies, particularly in detecting and classifying lung diseases. This study delves into an extensive compilation of over 200 studies from the recent five years (2018–2023), employing advanced machine learning, including deep learning methodologies to analyze CXR images. Our exploration is twofold: it categorizes these studies based on the methods used and the types of lung diseases addressed. It also presents an in-depth examination of the current limitations and prospective trajectories in this rapidly evolving field. Our findings underscore the transformative impact and continual progress of deep learning models in enhancing the accuracy and efficiency of lung disease detection using CXR images. This survey culminates by emphasizing the critical need for further technological advancement in this domain, aiming to bridge gaps in healthcare provision and improve patient outcomes. The overarching goal is to pave the way for more precise, efficient, and accessible diagnostic tools in the battle against lung diseases, reinforcing the indispensable role of technology in modern healthcare.

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Authors and Affiliations

  1. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Mohammed A. A. Al-qaness & Jie Zhu

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Dalal AL-Alimi

  3. Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar, 01000, Algeria

    Abdelghani Dahou

  4. Insight Centre for Data Analytics, University of Galway, Galway, Ireland

    Saeed Hamood Alsamhi

  5. Faculty of Engineering, IBB University, IBB, Yemen

    Saeed Hamood Alsamhi

  6. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz

  7. Department of Computer, Damietta University, Damietta, 34517, Egypt

    Ahmed A. Ewees

  8. Zhejiang Optoelectronics Research Institute, 321004, Jinhua, China

    Mohammed A. A. Al-qaness

  9. Faculty of Computer Science & Engineering, Galala University, Galala University, 435611, Suze, Egypt

    Mohamed Abd Elaziz

  10. Artificial Intelligence Research Center (AIRC), Ajman University, 346, Ajman, United Arab Emirates

    Mohamed Abd Elaziz

  11. Department of Electrical and Computer Engineering, Lebanese American University, 13-5053, Byblos , Lebanon

    Mohamed Abd Elaziz

  12. School of Computer Science and Technology, Zhejiang Normal University, 321004, Jinhua, China

    Abdelghani Dahou

  13. MEU Research Unit, Middle East University, 11831, Amman, Jordan

    Mohamed Abd Elaziz

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Al-qaness, M.A.A., Zhu, J., AL-Alimi, D. et al. Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10081-y

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