Automated Diagnosis of COVID-19 and Pneumonia Using Deep Learning Techniques on Radiological Images

Kawther Sameer Ali and Ahmad Shaker Abdalrada

References:

1. M. Institute for Health and Evaluation, “Global burden of chronic respiratory diseases and risk factors, 1990–2019,” 2020. [Online]. Available: https://www.healthdata.org/research-analysis/library/global-burden-chronic-respiratory-diseases-and-risk-factors-1990-2019.
2. GBD Chronic Respiratory Disease Collaborators, “Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019,” eClinicalMedicine, vol. 61, p. 101936, Apr. 2023, doi: 10.1016/j.eclinm.2023.101936.
3. World Health Organization, “WHO Coronavirus (COVID-19) Dashboard,” 2024. [Online]. Available: https://covid19.who.int.
4. Cleveland Clinic, “Chest X-ray,” 2022. [Online]. Available: https://my.clevelandclinic.org/health/diagnostics/10228-chest-x-ray.
5. C. Luo, “Deep learning-based early detection of pneumonia and COVID-19 using chest X-ray,” Scientific Reports, vol. 14, Art. no. 60861, 2024. [Online]. Available: https://www.nature.com/articles/s41598-024-60861-6.
6. J. E. Brinkman and S. S. Brinkman, “Physiology, Pulmonary,” in StatPearls [Internet]. Treasure Island, FL, USA: StatPearls Publishing, 2023.
7. Centers for Disease Control and Prevention, “Underlying Conditions and the Higher Risk for Severe COVID-19,” 2023. [Online]. Available: https://www.cdc.gov/covid/hcp/clinical-care/underlying-conditions.html.
8. H. X. Bai et al., “Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT,” Radiology, 2020, doi: 10.1148/radiol.2020200823.
9. World Health Organization, “Pneumonia Fact Sheet,” 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/pneumonia.
10. MedlinePlus, “Pneumonia,” 2024. [Online]. Available: https://medlineplus.gov/pneumonia.html.
11. World Health Organization, “Novel Coronavirus—China,” 2020. [Online]. Available: https://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/.
12. H. Y. F. Wong et al., “Frequency and distribution of chest radiographic findings in COVID-19 positive patients,” Radiology, 2020, doi: 10.1148/radiol.2020201160.
13. Y. Fang et al., “Sensitivity of chest CT for COVID-19: comparison to RT-PCR,” Radiology, vol. 296, no. 2, pp. E115–E117, 2020.
14. M. Chung et al., “CT imaging features of 2019 novel coronavirus (2019-nCoV),” Radiology, vol. 295, no. 1, pp. 202–207, 2020.
15. A. Sodickson et al., “Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults,” Radiology, vol. 251, no. 1, pp. 175–184, 2009.
16. P. Rajpurkar et al., “CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning,” arXiv, 2017. [Online]. Available: https://arxiv.org/abs/1711.05225.
17. A. S. Musallam, A. S. Sherif, and M. K. Hussein, “Efficient framework for detecting COVID-19 and pneumonia from chest X-ray using deep convolutional network,” Egyptian Informatics Journal, vol. 23, no. 2, pp. 247–257, Jul. 2022, doi: 10.1016/j.eij.2022.01.002.
18. L. Rasmy et al., “Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction,” NPJ Digital Medicine, vol. 4, no. 1, p. 86, 2021.
19. A. Abdalrada, A. F. Neamah, and H. Murad, “Predicting diabetes disease occurrence using logistic regression: An early detection approach,” Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 1, pp. 160–167, 2024.
20. Z. A. Anwer and A. S. Abdalrada, “Assessing institutional performance using machine learning on Arabic Facebook comments,” Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 16025–16031, 2024.
21. A. S. Abdalrada et al., “Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: A retrospective cohort study,” Journal of Diabetes & Metabolic Disorders, vol. 21, no. 1, pp. 251–261, 2022.
22. A. S. Abdalrada et al., “Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features,” in Proc. 30th Int. Conf. Computer Applications in Industry and Engineering (CAINE), 2017, pp. 169–175.
23. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: An overview and application in radiology,” Insights into Imaging, 2018, doi: 10.1007/s13244-018-0639-9.
24. A. Sharma and A. Guleria, “A deep learning based model for the detection of pneumonia from chest X-ray images using VGG-16 and neural networks,” Procedia Computer Science, 2023.
25. Y. Zhang, “Confidence aware anomaly detection (CAAD) model for detecting viral pneumonia from chest X-rays,” Computers in Biology and Medicine, 2023.
26. M. Goyal and D. Singh, “RNN and LSTM-based technique for image enhancement and region-of-interest extraction in pneumonia detection,” Journal of Ambient Intelligence and Humanized Computing, 2022, doi: 10.1007/s12652-021-03464-7.
27. F. Hamdi and T. S. Kandil, “Detection of COVID-19 and pneumonia in chest X-ray images using deep learning techniques,” 2024, doi: 10.1109/ICCI61671.2024.10485175.
28. M. D. K. Hasan et al., “Deep learning approaches for detecting pneumonia in COVID-19 patients by analyzing chest X-ray images,” Mathematical Problems in Engineering, 2021, doi: 10.1155/2021/9929274.
29. S. V. Militante, N. V. Dionisio, and B. G. Sibbaluca, “Pneumonia and COVID-19 detection using convolutional neural networks,” in Proc. IEEE ICVEE, 2020, pp. 1–6.
30. K. Hammoudi, “Deep learning on chest X-ray images to detect and evaluate pneumonia cases,” Journal of Medical Systems, 2021, doi: 10.1007/s10916-021-01745-4.
31. A. I. Khan and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images,” Computers in Biology and Medicine, 2021.
32. A. Manickam, “Automated pneumonia detection on chest X-ray images,” Measurement, 2021, doi: 10.1016/j.measurement.2021.110506.
33. C. Ieracitano and N. Mammone, “CovNNet: A deep learning model combining fuzzy logic and CNNs for COVID-19 and pneumonia diagnosis,” Neurocomputing, 2020.
34. T. Rahman, “COVID-19 Radiography Database,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
35. P. T. Mooney, “Chest X-Ray Images (Pneumonia),” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.
36. R. H. Wiggins et al., “Image file formats: past, present, and future,” Radiographics, vol. 21, no. 3, pp. 789–798, 2001.
37. C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, p. 60, 2019, doi: 10.1186/s40537-019-0197-0.
38. B. M. Hussein and S. M. Shareef, “An empirical study on the correlation between early stopping patience and epochs in deep learning,” in ITM Web of Conferences, vol. 64, p. 01003, 2024, doi: 10.1051/itmconf/20246401003.
39. H. Guo, J. Jin, and B. Liu, “Stochastic weight averaging revisited,” Applied Sciences, vol. 13, no. 5, p. 2935, 2023, doi: 10.3390/app13052935.
40. C. Tan et al., “A survey on deep transfer learning,” in Lecture Notes in Computer Science, vol. 11141, 2018
41. M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Machine Learning, 2019, pp. 6105–6114.
42. R. R. Selvaraju et al., “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Computer Vision, 2017, pp. 618–626.
43. E. Tartaglione et al., “Unveiling COVID-19 from chest X-ray with deep learning: A hurdles race with small data,” arXiv, 2020. [Online]. Available: https://arxiv.org/abs/2004.05405.
44. M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Information Processing & Management, 2009, doi: 10.1016/j.ipm.2009.03.002.
45. A. A. Taha and A. Hanbury, “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool,” BMC Medical Imaging, 2015, doi: 10.1186/s12880-015-0068-x.