Artificial Intelligence-Based Pneumonia Detection via Chest X-Ray – A State-of-the-Art Review


  • Neelkant Newra Department of Biomedical Engineering, National Institute of Technology, Raipur, C.G., India
  • Lingamgunta Saikumar Department of Biomedical Engineering, National Institute of Technology, Raipur, C.G., India
  • Saurabh Gupta Department of Biomedical Engineering, National Institute of Technology, Raipur, C.G., India
  • Sumit Kumar Banchhor Department of Biomedical Engineering, National Institute of Technology, Raipur, C.G., India


Artificial intelligence, pneumonia, chest X-ray, machine learning, deep learning, transfer learning, state-of-the-art


Artificial intelligence (AI) has emerged as a useful tool for early detection of pneumonia disease in the lungs using chest X-ray (CXR). For pneumonia detection different machine learning, deep learning, and transfer learning algorithms are used but a detailed review comparing the dataset with literature is lacking. This review paper first briefly summarizes different AI-based algorithms on classification, regression, and clustering. Then a detailed comparison of current literature on the ground of different reliable datasets and techniques are presented. Lastly, major challenges faced over the last few years are discussed with their future scopes. Our main objective is to provide a state-of-the-art review of the AI studies detecting pneumonia disease in CXR using data comparison and find the limitations to make suggestions for practitioners.


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Collection of images in the various dataset




How to Cite

Newra, N. ., Saikumar, L. ., Gupta, S. ., & Kumar Banchhor, S. . (2023). Artificial Intelligence-Based Pneumonia Detection via Chest X-Ray – A State-of-the-Art Review. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 437–448. Retrieved from



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