Journal Article2025Cited by 3

Lung disease diagnosis from CXR using convolutional neural network (CNN): A comparative study

Siam Tahsin Bhuiyan, Rashedur Rahman, Rubaiyat Islam, Mahmudul Haque, Mehnaz Islam, Syoji Kobashi, Saadia Binte Alam

Soft Computing

Springer, Vol. 29, Issue 23, pp. 6333-6345, ISBN: 1432-7643

  • Good Health and Well-being — Relevance: 86%
DOI: 10.1007/s00500-025-10912-5URL

Keywords

  • viral pneumonia
  • Tuberculosis
  • lung disease diagnosis
  • convolutional neural networks
  • lung opacity
  • ResNet
  • MobileNetV2
  • COVID-19 pandemic
  • chest radiography
  • DenseNet

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

  • Bachelors

    Independent University, Bangladesh (IUB)

Published at

Scientific Reports

Md Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

  • Assistant Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

  • Associate Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

References

  1. 1.Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2016). Deep Residual Learning for Image Recognition. , 770–778[10.1109/cvpr.2016.90]
  2. 2.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255[10.1109/cvpr.2009.5206848]
  3. 3.Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q. Weinberger. (2017). Densely Connected Convolutional Networks. , 2261–2269[10.1109/cvpr.2017.243]
  4. 4.Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. , 4510–4520[10.1109/cvpr.2018.00474]
  5. 5.Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang. (2016). Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?. IEEE Transactions on Medical Imaging, 35(5), 1299–1312[10.1109/tmi.2016.2535302]
  6. 6.Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi, Mamun Bin Ibne Reaz, Mohammad Tariqul Islam. (2020). Can AI Help in Screening Viral and COVID-19 Pneumonia?. IEEE Access, 8, 132665–132676[10.1109/access.2020.3010287]
  7. 7.Tawsifur Rahman, Amith Khandakar, Yazan Qiblawey, Anas Tahir, Serkan Kiranyaz, Saad Bin Abul Kashem, Mohammad Tariqul Islam, Somaya Al Maadeed, Susu M. Zughaier, Muhammad Salman Khan, Muhammad E.H. Chowdhury. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in Biology and Medicine, 132, 104319[10.1016/j.compbiomed.2021.104319]
  8. 8.Olli Ruuskanen, Elina Lahti, Lance C Jennings, David R Murdoch. (2011). Viral pneumonia. The Lancet, 377(9773), 1264–1275[10.1016/s0140-6736(10)61459-6]
  9. 9.Gian Battista Danzi, Marco Loffi, Gianluca Galeazzi, Elisa Gherbesi. (2020). Acute pulmonary embolism and COVID-19 pneumonia: a random association?. European Heart Journal, 41(19), 1858[10.1093/eurheartj/ehaa254]
  10. 10.Tawsifur Rahman, Amith Khandakar, Muhammad Abdul Kadir, Khandaker Rejaul Islam, Khandakar F. Islam, Rashid Mazhar, Tahir Hamid, Mohammad Tariqul Islam, Saad Kashem, Zaid Bin Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. (2020). Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization. IEEE Access, 8, 191586–191601[10.1109/access.2020.3031384]
  11. 11.Adrian P. Brady. (2016). Error and discrepancy in radiology: inevitable or avoidable?. Insights into Imaging, 8(1), 171–182[10.1007/s13244-016-0534-1]
  12. 12.Simeon Spasov, Luca Passamonti, Andrea Duggento, Pietro Liò, Nicola Toschi. (2019). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. NeuroImage, 189, 276–287[10.1016/j.neuroimage.2019.01.031]
  13. 13.Tyler J. Bradshaw, Zachary Huemann, Junjie Hu, Arman Rahmim. (2023). A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging. Radiology Artificial Intelligence, 5(4), e220232[10.1148/ryai.220232]
  14. 14.B. van Ginneken, S. Katsuragawa, B.M. ter Haar Romeny, Kunio Doi, M.A. Viergever. (2002). Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Transactions on Medical Imaging, 21(2), 139–149[10.1109/42.993132]
  15. 15.Yasser Sakr, Manuela Giovini, Marc Leone, Giacinto Pizzilli, Andreas Kortgen, Michael Bauer, Tommaso Tonetti, Gary Duclos, Laurent Zieleskiewicz, Samuel Buschbeck, V. Marco Ranieri, Elio Antonucci. (2020). Pulmonary embolism in patients with coronavirus disease-2019 (COVID-19) pneumonia: a narrative review. Annals of Intensive Care, 10(1), 124[10.1186/s13613-020-00741-0]
  16. 16.Susannah McLean, Ulugbek Nurmatov, Joseph LY Liu, Claudia Pagliari, Josip Car, Aziz Sheikh. (2012). Telehealthcare for chronic obstructive pulmonary disease: Cochrane Review and meta-analysis. British Journal of General Practice, 62(604), e739–e749[10.3399/bjgp12x658269]
  17. 17.Junji Shiraishi, Qiang Li, Daniel Appelbaum, Kunio Doi. (2011). Computer-Aided Diagnosis and Artificial Intelligence in Clinical Imaging. Seminars in Nuclear Medicine, 41(6), 449–462[10.1053/j.semnuclmed.2011.06.004]
  18. 18.FM Javed Mehedi Shamrat, Sami Azam, Asif Karim, Kawsar Ahmed, Francis M. Bui, Friso De Boer. (2023). High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. Computers in Biology and Medicine, 155, 106646[10.1016/j.compbiomed.2023.106646]
  19. 19.Vinayakumar Ravi, Vasundhara Acharya, Mamoun Alazab. (2022). A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images. Cluster Computing, 26(2), 1181–1203[10.1007/s10586-022-03664-6]
  20. 20.Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber. (2020). DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks. IEEE Access, 8, 74901–74913[10.1109/access.2020.2989273]
  21. 21.Andrew J. Degnan, Emily H. Ghobadi, Peter Hardy, Elizabeth Krupinski, Elena P. Scali, Lindsay Stratchko, Adam Ulano, Eric Walker, Ashish P. Wasnik, William F. Auffermann. (2018). Perceptual and Interpretive Error in Diagnostic Radiology—Causes and Potential Solutions. Academic Radiology, 26(6), 833–845[10.1016/j.acra.2018.11.006]
  22. 22.Harshal A. Sanghvi, Riki H. Patel, Ankur Agarwal, Shailesh Gupta, Vivek Sawhney, Abhijit S. Pandya. (2022). A deep learning approach for classification of COVID and pneumonia using DenseNet ‐201. International Journal of Imaging Systems and Technology, 33(1), 18–38[10.1002/ima.22812]
  23. 23.Mohammed Salih Ahmed, Atta Rahman, Faris AlGhamdi, Saleh AlDakheel, Hammam Hakami, Ali AlJumah, Zuhair AlIbrahim, Mustafa Youldash, Mohammad Aftab Alam Khan, Mohammed Imran Basheer Ahmed. (2023). Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach. Diagnostics, 13(15), 2562[10.3390/diagnostics13152562]
  24. 24.Bo Li, Jie Shen, Liang Li, Chengxin Yu. (2020). Radiographic and Clinical Features of Children With Coronavirus Disease (COVID-19) Pneumonia. Indian Pediatrics, 57(5), 423–426[10.1007/s13312-020-1816-8]
  25. 25.M.K. Pandit, S.A. Banday, R. Naaz, M.A. Chishti. (2020). Automatic detection of COVID-19 from chest radiographs using deep learning. Radiography, 27(2), 483–489[10.1016/j.radi.2020.10.018]
  26. 26.Marios Constantinou, Themis Exarchos, Aristidis G. Vrahatis, Panagiotis Vlamos. (2023). COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods. International Journal of Environmental Research and Public Health, 20(3), 2035[10.3390/ijerph20032035]
  27. 27.Lara Visuña, Dandi Yang, Javier Garcia-Blas, Jesus Carretero. (2022). Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Medical Imaging, 22(1), 178[10.1186/s12880-022-00904-4]
  28. 28.Enas M. F. El Houby. (2024). COVID‑19 detection from chest X-ray images using transfer learning. Scientific Reports, 14(1), 11639[10.1038/s41598-024-61693-0]
  29. 29.Dan Pan, An Zeng, Baoyao Yang, Gangyong Lai, Bing Hu, Xiaowei Song, Tianzi Jiang, Alzheimer's Disease Neuroimaging Initiative (ADNI). (2022). Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease. Advanced Science, 10(6), e2204717[10.1002/advs.202204717]
  30. 30.Hajar Lamouadene, Majid EL Kassaoui, Mourad El Yadari, Abdallah El Kenz, Abdelilah Benyoussef, Amine El Moutaouakil, Omar Mounkachi. (2025). Detection of COVID-19, lung opacity, and viral pneumonia via X-ray using machine learning and deep learning. Computers in Biology and Medicine, 191, 110131[10.1016/j.compbiomed.2025.110131]
  31. 31.Kazutoshi Ukai, Rashedur Rahman, Naomi Yagi, Keigo Hayashi, Akihiro Maruo, Hirotsugu Muratsu, Syoji Kobashi. (2021). Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Scientific Reports, 11(1), 11716[10.1038/s41598-021-91144-z]
  32. 32.Mohammadreza Zandehshahvar, Marly van Assen, Hossein Maleki, Yashar Kiarashi, Carlo N. De Cecco, Ali Adibi. (2021). Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease. Scientific Reports, 11(1), 11112[10.1038/s41598-021-90411-3]
  33. 33.Sarah I. Kamel, David C. Levin, Laurence Parker, Vijay M. Rao. (2016). Utilization Trends in Noncardiac Thoracic Imaging, 2002-2014. Journal of the American College of Radiology, 14(3), 337–342[10.1016/j.jacr.2016.09.039]
  34. 34.Wael AbdAlmageed, Hengameh Mirzaalian, Xiao Guo, Linda M. Randolph, Veeraya K. Tanawattanacharoen, Mitchell E. Geffner, Heather M. Ross, Mimi S. Kim. (2020). Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning. JAMA Network Open, 3(11), e2022199[10.1001/jamanetworkopen.2020.22199]
  35. 35.Judith Juan, Eduard Monsó, Carme Lozano, Marta Cufí, Paula Subías-Beltrán, Laura Ruiz-Dern, Xavier Rafael-Palou, Marta Andreu, Eva Castañer, Xavier Gallardo, Anna Ullastres, Carles Sans, Manel Lujàn, Carles Rubiés, Vicent Ribas-Ripoll. (2023). Computer-assisted diagnosis for an early identification of lung cancer in chest X rays. Scientific Reports, 13(1), 7720[10.1038/s41598-023-34835-z]
  36. 36.Rakhi Wajgi, Ganesh Yenurkar, Vincent O. Nyangaresi, Badal Wanjari, Sanjana Verma, Arya Deshmukh, Somesh Mallewar. (2024). Optimized tuberculosis classification system for chest X‐ray images: Fusing hyperparameter tuning with transfer learning approaches. Engineering Reports, 6(11)[10.1002/eng2.12906]
  37. 37.V. Padmavathi, Kavitha Ganesan. (2025). LungNet-ViT: Efficient lung disease classification using a multistage vision transformer model from chest radiographs. Journal of X-Ray Science and Technology, 33(4), 742–759[10.1177/08953996251320262]
  38. 38.Niharika Singh, Satish Hamde. (2019). Tuberculosis Detection Using Shape and Texture Features of Chest X-Rays. Lecture notes in networks and systems, 43–50[10.1007/978-981-13-3765-9_5]
  39. 39.Lucas Gabriel Coimbra Evalgelista, Elloá B. Guedes. (2018). Computer-Aided Tuberculosis Detection from Chest X-Ray Images with Convolutional Neural Networks. , 518–527[10.5753/eniac.2018.4444]
  40. 40.J. Melendez, C. I. Sánchez, R. H. H. M. Philipsen, P. Maduskar, B. van Ginneken. (2014). Multiple-instance learning for computer-aided detection of tuberculosis. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, 9035, 90351J[10.1117/12.2043018]
  41. 41.Nadya Novalina, Mia Rizkinia. (2023). Implementation of Xception and EfficientNetB3 for COVID-19 Detection on Chest X-Ray Image via Transfer Learning. International Journal of Electrical Computer and Biomedical Engineering, 1(2), 97–107[10.62146/ijecbe.v1i2.14]
  42. 42.United Nations Children's Fund. (2016). Statistical Tables. ˜The œState of the world's children, 107–172[10.18356/d16f2a12-en]
  43. 43.Rajpurkar, Pranav, Irvin, Jeremy, Zhu, Kaylie, Yang, Brandon, Mehta, Hershel, Duan, Tony, Ding, Daisy, Bagul, Aarti, Langlotz, Curtis, Shpanskaya, Katie, Lungren, Matthew P., Ng, Andrew Y.. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep\n Learning. arXiv (Cornell University)[10.48550/arxiv.1711.05225]
  44. 44.Tahir, Anas, Qiblawey, Yazan, Khandakar, Amith, Rahman, Tawsifur, Khurshid, Uzair, Musharavati, Farayi, Islam, M. T., Kiranyaz, Serkan, Chowdhury, Muhammad E. H.. (2020). Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images. arXiv (Cornell University)[10.48550/arxiv.2005.11524]

Cited By

  1. 1.Siam Tahsin Bhuiyan, Rashedur Rahman, Sefatul Wasi, Halima Khatun, Manosh Sur Chowdhury, Riyadul Islam, Samiul Karim Mazumder, Naomi Yagi, Syoji Kobashi, Saadia Binte Alam. (2026). Beyond the pelvic ring: benchmarking multi-bone segmentation for ROI-Guided fracture detection. Scientific Reports, 16(1)[10.1038/s41598-026-47663-8]
  2. 2.Sayeda Subha Ashrafi, Tushar Ahmed Shishir. (2026). Deep Learning-Based Automated Breast Cancer Detection from Histopathology Images: Benchmarking CNN and Transformer Architectures. , 1–6[10.1109/peccii70991.2026.11662018]
  3. 3.Siam Tahsin Bhuiyan, Zayan Zaman, Rashedur Rahman, Manosh Sur Choudhury, Sefatul Wasi, Ashraful Islam, Saadia Binte Alam, Syoji Kobashi. (2026). CheXNet-CBAM: Enhancing Large-scale Medical Pre-training with Attention for Lung Disease Diagnosis. , 105–110[10.1109/ismvl68998.2026.00027]
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