Pneumonia Detection Using Transfer Learning: A Systematic Literature Review

Deep learning models have significantly improved pneumonia detection using X-ray image analysis in the field of AI-driven healthcare, showing a major advancement in the effectiveness of medical decision systems. In this paper, we have conducted a systematic literature review of pneumonia detection techniques that applied transfer learning combined with other methods. The review protocol has been developed thoroughly and it identifies recent research related to pneumonia detection from the past five years. We have used very famous research repositories such as IEEE, Elsevier, Springer, and ACM digital library. After a thorough search process, 35 papers are finalized. The review summarizes those past papers that have implemented different methods of pneumonia detection and results are compared based on the best performing models. Also, these models have been categorized into three approaches to pneumonia detection: Deep Learning methods, Transfer Learning techniques, and hybrid methods. Then, there is a performance comparison of the best-performing models for pneumonia detection. This study concludes that while transfer learning holds substantial potential for improving pneumonia detection, further research is necessary to optimize these models for clinical application. This study concludes that while transfer learning holds substantial potential for improving pneumonia detection, further research is necessary to optimize these models for clinical application. This review is very helpful for the researchers in identifying the research gap for pneumonia detection techniques and how these gaps can be addressed shortly.

@article{Abueed2025,
  title     = {Pneumonia Detection Using Transfer Learning: A Systematic Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mohammed A M Abueed and Danial Md Nor and Nabilah Ibrahim and Jean-Marc Ogier},
  doi       = {10.14569/IJACSA.2025.01602102},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602102}
}