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Original article

Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning

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Total Article Reads Cureus PMC
12,002 Article Views 6,071 4,175
PDF Downloads 1,067 689
Total Article Reads Cureus PMC
12,002 Article Views 6,071 4,175
PDF Downloads 1,067 689
Total Article Reads
Cureus PMC
Article Views 6,071 4,175
PDF Downloads 1,067 689

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Crossref Citations (63)
  1. Luján-García, J. E., Moreno-Ibarra, M. A., Villuendas-Rey, Y., & Yáñez-Márquez, C. (2020). Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images. Mathematics, 8(9), 1423. doi:10.3390/math8091423.
  2. Kiener, M. (2020). Artificial intelligence in medicine and the disclosure of risks. AI & SOCIETY. doi:10.1007/s00146-020-01085-w.
  3. Kikkisetti, S., Zhu, J., Shen, B., Li, H., & Duong, T. Q. (2020). Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs. PeerJ, 8, e10309. doi:10.7717/peerj.10309.
  4. Suri, J. S., Agarwal, S., Gupta, S. K., Puvvula, A., Biswas, M., Saba, L., … Naidu, S. (2021). A Narrative Review on Characterization of Acute Respiratory Distress Syndrome in COVID-19-infected Lungs using Artificial Intelligence. Computers in Biology and Medicine, 104210. doi:10.1016/j.compbiomed.2021.104210.
  5. Musleh, A. A. waha. A., & Maghari, A. Y. (2020). COVID-19 Detection in X-ray Images using CNN Algorithm. 2020 International Conference on Promising Electronic Technologies (ICPET). doi:10.1109/ICPET51420.2020.00010.
  6. Alafif, T., Tehame, A. M., Bajaba, S., Barnawi, A., & Zia, S. (2021). Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. International Journal of Environmental Research and Public Health, 18(3), 1117. doi:10.3390/ijerph18031117.
  7. Wang, S.-H., Zhang, Y., Cheng, X., Zhang, X., & Zhang, Y.-D. (2021). PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation. Computational and Mathematical Methods in Medicine, 2021, 1–18. doi:10.1155/2021/6633755.
  8. Singh, G., & Yow, K.-C. (2021). These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition. IEEE Access, 9, 41482–41493. doi:10.1109/ACCESS.2021.3064838.
  9. Lv, D., Wang, Y., Wang, S., Zhang, Q., Qi, W., Li, Y., & Sun, L. (2021). A Cascade‐SEME network for COVID‐19 detection in chest c‐ray images. Medical Physics. doi:10.1002/mp.14711.
  10. Yousefi, P., & Jin, Y.-F. (2021). Image-Based Prediction of Respiratory Diseases Including COVID-19 Using Convolutional Neural Networks. Information Science and Applications, 189–200. doi:10.1007/978-981-33-6385-4_18.
  11. Lu, J. Q., Musheyev, B., Peng, Q., & Duong, T. Q. (2021). Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study. PeerJ, 9, e11205. doi:10.7717/peerj.11205.
  12. Chen, A., Zhao, Z., Hou, W., Singer, A. J., Li, H., & Duong, T. Q. (2021). Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study. Frontiers in Medicine, 8. doi:10.3389/fmed.2021.661940.
  13. Das, N., Topalovic, M., & Janssens, W. (2021). AIM in Respiratory Disorders. Artificial Intelligence in Medicine, 1–14. doi:10.1007/978-3-030-58080-3_178-1.
  14. Zandehshahvar, M., van Assen, M., Maleki, H., Kiarashi, Y., De Cecco, C. N., & Adibi, A. (2021). Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease. Scientific Reports, 11(1). doi:10.1038/s41598-021-90411-3.
  15. Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G., & Murphy, K. (2021). Deep Learning for Chest X-ray Analysis: A Survey. Medical Image Analysis, 102125. doi:10.1016/j.media.2021.102125.
  16. Li, Y., Cao, G., & Cao, W. (2021). Sample Efficient Lung Segmentation Using Group Structured Conditional Variational Data Imputation. 2021 IEEE International Conference on Multimedia and Expo (ICME). doi:10.1109/ICME51207.2021.9428076.
  17. Singh, G., & Yow, K.-C. (2021). An Interpretable Deep Learning Model for Covid-19 Detection With Chest X-Ray Images. IEEE Access, 9, 85198–85208. doi:10.1109/ACCESS.2021.3087583.
  18. Chaturvedi, K., Kansal, T., Gupta, S., Vishwakarma, D. K., & Deo, N. (2021). COVID-19 Severity Assessment from Chest X-rays using Attention-based Weakly-Supervised Learning. 2021 2nd International Conference for Emerging Technology (INCET). doi:10.1109/INCET51464.2021.9456449.
  19. Shah, H., Shah, S., Tanwar, S., Gupta, R., & Kumar, N. (2021). Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. Multimedia Systems. doi:10.1007/s00530-021-00818-1.
  20. Yousefi, B., Kawakita, S., Amini, A., Akbari, H., Advani, S. M., Akhloufi, M., … Ahadian, S. (2021). Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. Journal of Clinical Medicine, 10(14), 3100. doi:10.3390/jcm10143100.
  21. Kulagin, V., Akimov, D., Guryanova, E., & Pavelyev, S. (2021). Noisy Medical Images Aggregation for Pulmonary Tissue Damage Detection. Lecture Notes in Networks and Systems, 384–389. doi:10.1007/978-3-030-77448-6_36.
  22. Deng, H., & Li, X. (2021). AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review. Frontiers in Artificial Intelligence, 4. doi:10.3389/frai.2021.612914.
  23. Karthik, R., Menaka, R., Hariharan, M., & Kathiresan, G. S. (2021). AI for COVID-19 Detection from radiographs: Incisive analysis of state of the art techniques, key challenges and future directions. IRBM. doi:10.1016/j.irbm.2021.07.002.
  24. Perumal, V., Narayanan, V., & Rajasekar, S. J. S. (2021). Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models. Computer Methods and Programs in Biomedicine, 106336. doi:10.1016/j.cmpb.2021.106336.
  25. Wang, S.-H., Satapathy, S. C., Anderson, D., Chen, S.-X., & Zhang, Y.-D. (2021). Deep Fractional Max Pooling Neural Network for COVID-19 Recognition. Frontiers in Public Health, 9. doi:10.3389/fpubh.2021.726144.
  26. Khan, M., Mehran, M. T., Haq, Z. U., Ullah, Z., Naqvi, S. R., Ihsan, M., & Abbass, H. (2021). Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Systems with Applications, 185, 115695. doi:10.1016/j.eswa.2021.115695.
  27. Gomes, D. P. S., Ulhaq, A., Paul, M., Horry, M. J., Chakraborty, S., Saha, M., … Motiur Rahaman, D. M. (2021). Features Of ICU Admission In X-Ray Images Of Covid-19 Patients. 2021 IEEE International Conference on Image Processing (ICIP). doi:10.1109/ICIP42928.2021.9506266.
  28. Signoroni, A., Savardi, M., Benini, S., Adami, N., Leonardi, R., Gibellini, P., … Farina, D. (2021). BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Medical Image Analysis, 71, 102046. doi:10.1016/j.media.2021.102046.
  29. Chahar, S., & Roy, P. K. (2021). COVID-19: A Comprehensive Review of Learning Models. Archives of Computational Methods in Engineering. doi:10.1007/s11831-021-09641-3.
  30. Sayed, S. A.-F., Elkorany, A. M., & Sayed Mohammad, S. (2021). Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity. IEEE Access, 9, 135697–135707. doi:10.1109/ACCESS.2021.3116067.
  31. Horry, M. J., Chakraborty, S., Pradhan, B., Fallahpoor, M., … Chegeni, H. (2021). Factors determining generalization in deep learning models for scoring COVID-CT images. Mathematical Biosciences and Engineering, 18(6), 9264–9293. doi:10.3934/mbe.2021456.
  32. Bin Keram, U., bin Mohd Ramli, M. A., Kamal, N. A. M., & bin Mohd Abas, L. H. (2021). Covid-19 Detection from Chest X-Ray Images using Convolutional Neural Network. 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS). doi:10.1109/AiDAS53897.2021.9574345.
  33. Wang, S.-H., Zhu, Z., & Zhang, Y.-D. (2021). PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis. Frontiers in Public Health, 9. doi:10.3389/fpubh.2021.768278.
  34. Khanna, V. V., Chadaga, K., Sampathila, N., Prabhu, S., Chadaga, R., & Umakanth, S. (2022). Diagnosing COVID-19 using artificial intelligence: a comprehensive review. Network Modeling Analysis in Health Informatics and Bioinformatics, 11(1). https://doi.org/10.1007/s13721-022-00367-1 doi:10.1007/s13721-022-00367-1.
  35. Suri, J., Agarwal, S., Gupta, S., Puvvula, A., Viskovic, K., Suri, N., … Naidu, D. S. (2021). Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective. IEEE Journal of Biomedical and Health Informatics, 25(11), 4128–4139. doi:10.1109/JBHI.2021.3103839.
  36. Kumah, A., & Abuomar, O. (2021). Comparative Analysis of Machine Learning Algorithms Using COVID-19 Chest X-ray Images and Dataset. Intelligent Systems and Applications, 502–516. doi:10.1007/978-3-030-82199-9_33.
  37. Shen, J., Liu, F., Xu, M., Fu, L., Dong, Z., & Wu, J. (2022). Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks. Expert Systems with Applications, 116547. doi:10.1016/j.eswa.2022.116547.
  38. Asgharnezhad, H., Shamsi, A., Alizadehsani, R., Khosravi, A., Nahavandi, S., Sani, Z. A., … Islam, S. M. S. (2022). Objective evaluation of deep uncertainty predictions for COVID-19 detection. Scientific Reports, 12(1). doi:10.1038/s41598-022-05052-x.
  39. Muhammad, L. J., Amshi, J. M., Usman, S. S., Badi, I. A., Mohammed, I. A., Dada, O. S., & Haruna, A. A. (2021). Deep Learning Models for Classification and Localization of COVID-19 Abnormalities on Chest Radiographs. 2021 4th International Conference on Computing & Information Sciences (ICCIS). doi:10.1109/ICCIS54243.2021.9676401.
  40. Hasoon, J. N., Fadel, A. H., Hameed, R. S., Mostafa, S. A., Khalaf, B. A., Mohammed, M. A., & Nedoma, J. (2021). COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images. Results in Physics, 31, 105045. doi:10.1016/j.rinp.2021.105045.
  41. Ahmad, J., Saudagar, A. K. J., Malik, K. M., Ahmad, W., Khan, M. B., Hasanat, M. H. A., … Sajjad, M. (2022). Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans. International Journal of Environmental Research and Public Health, 19(1), 480. doi:10.3390/ijerph19010480.
  42. Aboutalebi, H., Pavlova, M., Shafiee, M. J., Sabri, A., Alaref, A., & Wong, A. (2021). COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images. Diagnostics, 12(1), 25. doi:10.3390/diagnostics12010025.
  43. Hijazi, H., Abu Talib, M., Hasasneh, A., Bou Nassif, A., Ahmed, N., & Nasir, Q. (2021). Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19. Sensors, 21(24), 8424. doi:10.3390/s21248424.
  44. Zhang, J., Yan, Y., Ni, H., & Ni, Z. (2021). Lung detection and severity prediction of pneumonia patients based on COVID-19 DET-PRE network. Expert Review of Medical Devices, 1–10. doi:10.1080/17434440.2022.2014319.
  45. Frid-Adar, M., Amer, R., Gozes, O., Nassar, J., & Greenspan, H. (2021). COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring. IEEE Journal of Biomedical and Health Informatics, 25(6), 1892–1903. doi:10.1109/JBHI.2021.3069169.
  46. Saha, I., Gourisaria, M. K., & Harshvardhan, G. (2021). Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). doi:10.1109/ICAIS50930.2021.9395961.
  47. Iqbal, S., Ayesha, H., Farooq Khan Niazi, M., Ayesha, N., & Tehseen Ahmad, K. (2022). COVID-19 Prediction, Diagnosis and Prevention Through Computer Vision. Prognostic Models in Healthcare: AI and Statistical Approaches, 79–113. https://doi.org/10.1007/978-981-19-2057-8_4 doi:10.1007/978-981-19-2057-8_4.
  48. Das, N., Topalovic, M., & Janssens, W. (2022). AIM in Respiratory Disorders. Artificial Intelligence in Medicine, 759–772. https://doi.org/10.1007/978-3-030-64573-1_178 doi:10.1007/978-3-030-64573-1_178.
  49. Tricarico, D., Calandri, M., Barba, M., Piatti, C., Geninatti, C., Basile, D., Gatti, M., Melis, M., & Veltri, A. (2022). Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results. Diagnostics, 12(3), 570. https://doi.org/10.3390/diagnostics12030570 doi:10.3390/diagnostics12030570.
  50. Amrutha Tejaswini, M., & Kommineni, M. (2022). Analysis and Detection of COVID-19 Using Various CNN Models. Algorithms for Intelligent Systems, 165–179. https://doi.org/10.1007/978-981-16-6460-1_12 doi:10.1007/978-981-16-6460-1_12.
  51. Subramaniam, U., Subashini, M. M., Almakhles, D., Karthick, A., & Manoharan, S. (2021). An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques. BioMed Research International, 2021, 1–17. https://doi.org/10.1155/2021/1896762 doi:10.1155/2021/1896762.
  52. Hadi, A., Pahlevi, R. R., & Suryani, V. (2021). Early Warning System for Physical Distancing Detection in the Prevention of COVID-19 Spread. 2021 International Conference on Data Science and Its Applications (ICoDSA). https://doi.org/10.1109/icodsa53588.2021.9617553 doi:10.1109/ICoDSA53588.2021.9617553.
  53. Bhalodia, R., Hatamizadeh, A., Tam, L., Xu, Z., Wang, X., Turkbey, E., & Xu, D. (2021). Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports. Lecture Notes in Computer Science, 571–581. https://doi.org/10.1007/978-3-030-87196-3_53 doi:10.1007/978-3-030-87196-3_53.
  54. Alqahtani, M. S., Abbas, M., Alqahtani, A., Alshahrani, M., Alkulib, A., Alelyani, M., Almarhaby, A., & Alsabaani, A. (2021). A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images. Diagnostics, 11(5), 855. https://doi.org/10.3390/diagnostics11050855 doi:10.3390/diagnostics11050855.
  55. Singh, G., & Yow, K.-C. (2021). Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images. Diagnostics, 11(9), 1732. https://doi.org/10.3390/diagnostics11091732 doi:10.3390/diagnostics11091732.
  56. Muhammad, L. J., Algehyne, E. A., Usman, S. S., Mohammed, I. A., Abdulkadir, A., Jibrin, M. B., & Malgwi, Y. M. (2021). Deep Learning Models for Predicting COVID-19 Using Chest X-Ray Images. EAI/Springer Innovations in Communication and Computing, 127–144. https://doi.org/10.1007/978-3-030-75945-2_6 doi:10.1007/978-3-030-75945-2_6.
  57. Coronavirus Pneumonia Classification using X-Ray and CT Scan Images with Deep Convolutional Neural Networks Models. (2022). Journal of Information Technology Research, 15(1), 0–0. https://doi.org/10.4018/jitr.299391 doi:10.4018/JITR.299391.
  58. Azade, A., & Anand, K. M. (2022). Impact of Image Augmentation in COVID-19 Detection Using Chest X-Ray Images. 2022 IEEE Delhi Section Conference (DELCON). https://doi.org/10.1109/delcon54057.2022.9752785 doi:10.1109/DELCON54057.2022.9752785.
  59. Wang, D., Willis, D. R., & Yih, Y. (2022). The pneumonia severity index: assessment and comparison to popular machine learning classifiers. International Journal of Medical Informatics, 104778. https://doi.org/10.1016/j.ijmedinf.2022.104778 doi:10.1016/j.ijmedinf.2022.104778.
  60. Sharma, H., Nagar, R., & Mishra, D. (2022). Data Driven Estimation of Covid-19 Prognosis. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). https://doi.org/10.1109/isbi52829.2022.9761406 doi:10.1109/ISBI52829.2022.9761406.
  61. Jothi Prabha, A., Venkateswaran, N., & Sengodan, P. (2022). AI-Based Deep Random Forest Ensemble Model for Prediction of COVID-19 and Pneumonia from Chest X-Ray Images. Artificial Intelligence for Innovative Healthcare Informatics, 133–149. https://doi.org/10.1007/978-3-030-96569-3_7 doi:10.1007/978-3-030-96569-3_7.
  62. Danilov, V. V., Litmanovich, D., Proutski, A., Kirpich, A., Nefaridze, D., Karpovsky, A., & Gankin, Y. (2022). Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-15013-z doi:10.1038/s41598-022-15013-z.
  63. Öksüz, C., Urhan, O., & Güllü, M. K. (2021). COVID‐19 detection with severity level analysis using the deep features, and wrapper‐based selection of ranked features. Concurrency and Computation: Practice and Experience, 34(20). Portico. https://doi.org/10.1002/cpe.6802 doi:10.1002/cpe.6802.

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