Medical Images Based Covid-19 Detection Survey
DOI:
https://doi.org/10.29304/jqcm.2023.15.3.1270Keywords:
Artificial intelligence, COVID-19 detection, Machine learning, Chest X-ray Prediction, Deep learningAbstract
In December 2019, COVID-19 appeared for the first time in Wuhan (Hubei Province, China), after which it quickly spread over the entire earth. The World Health Organization quickly designated COVID-19 a pandemic because of the high number of deaths and rapid global spread of the disease. Because of this, many facets of society have been impacted, and those effects may last for years to come. Therefore, COVID-19 detection methods were the focus of numerous research projects in the past. This has led to the development of the COVID-19 AI Detector, a specialized area of artificial intelligence-based research. In this paper, we survey all significant current efforts that have taken advantage of machine learning to COVID-19 detection and prediction. We first surveyed all datasets used in relevant research, and then summarized them in a table containing the link to that data. Then, we mention all the methodologies employed to detect the presence of COVID-19 using such datasets. Later, the challenges and difficulties that facing the concerned researches are reported, while the results of all interesting related work that lay ahead in this field before concluding this paper were discussed fairly
Downloads
Download data is not yet available.
References
[1] W. H. Organization, "Coronavirus disease (COVID-19) pandemic," https://www.who.int/emergencies/diseases/novel-coronavirus-2019, 2019.
[2] F. M. Shah et al., "A comprehensive survey of covid-19 detection using medical images," vol. 2, no. 6, p. 434, 2021.
[3] W. h. organization, "Listings of WHO’s response to COVID-19," https://www.who.int/news/item/29-06-2020-covidtimeline, 2020.
[4] A. M. Kadim, F. S. Al-Mukhtar, N. A. Hasan, A. B. Alnajjar, M. S. M. J. P. o. E. Altaei, and N. Sciences, "K-Means clustering of optimized wireless network sensor using genetic algorithm," vol. 10, no. 3, pp. 276-285, 2022.
[5] J. Shen et al., "A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine," vol. 5, 2022.
[6] E. I. F. B. I. RESEARCH, "COVID-19 imaging datasets, Overview of open access COVID-19 imaging datasets for teaching, training and research," https://www.eibir.org/covid-19-imaging-datasets/, 2021.
[7] A. H. T. Al-Ghrairi, A. A. Mohammed, and E. Z. J. I. I. J. o. A. I. Sameen, "Face detection and recognition with 180 degree rotation based on principal component analysis algorithm," vol. 11, no. 2, p. 593, 2022.
[8] M. Abdul Salam, S. Taha, and M. J. P. O. Ramadan, "COVID-19 detection using federated machine learning," vol. 16, no. 6, p. e0252573, 2021.
[9] S. Mandal, S. Mallick, A. Roy, and A. J. I. J. o. C. A. Paul, "AI based Automated Diagnosis of COVID-19 Patients," vol. 975, p. 8887.
[10] R. Harkness, G. Hall, A. F. Frangi, N. Ravikumar, and K. J. a. p. a. Zucker, "The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays," 2021.
[11] R. Qu, Y. Yang, and Y. Wang, "COVID-19 detection using CT image based on YOLOv5 network," in 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), 2021, pp. 622-625: IEEE.
[12] L. R. Baltazar et al., "Artificial intelligence on COVID-19 pneumonia detection using chest xray images," vol. 16, no. 10, p. e0257884, 2021.
[13] M. K. Hasan et al., "Challenges of deep learning methods for COVID-19 detection using public datasets," vol. 30, p. 100945, 2022.
[14] A. M. Ismael and A. J. E. S. w. A. Şengür, "Deep learning approaches for COVID-19 detection based on chest X-ray images," vol. 164, p. 114054, 2021.
[15] S. Mishra, H. K. Shukla, R. Singh, V. Pandey, S. Sagar, and Y. Singh, "Covid-19 Detection Using AI," 2021.
[16] M. Mahbub, M. Biswas, A. M. Miah, A. Shahabaz, and M. S. Kaiser, "Covid-19 detection using chest x-ray images with a regnet structured deep learning model," in International Conference on Applied Intelligence and Informatics, 2021, pp. 358-370: Springer.
[17] M. Mohamed, D. Hamam, and S. Ahmed, "COVID-19 Detection from Chest X-ray Images Using Artificial-Intelligence-Based Model Imported in a Mobile Application," 2021.
[18] S. Ali, A. Hussain, S. Bhattacharjee, A. Athar, and H.-C. J. S. Kim, "Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model," vol. 22, no. 24, p. 9983, 2022.
[19] V. J. T. Gupta, "COVID-19 Detection using Deep Learning," vol. 6, no. 12, pp. 421-425, 2020.
[20] J. Luo, Y. Sun, J. Chi, X. Liao, C. J. B. M. I. Xu, and D. Making, "A novel deep learning-based method for COVID-19 pneumonia detection from CT images," vol. 22, no. 1, pp. 1-7, 2022.
[21] S. Mahajan, A. Raina, X. Z. Gao, A. K. J. C. Pandit, C. Practice, and Experience, "COVID‐19 detection using hybrid deep learning model in chest x‐rays images," vol. 34, no. 5, p. e6747, 2022.
[22] E. Sankar, M. Y. Varma, and K. A. Kumar, "COVID-19 DETECTION USING CHEST X-RAY IMAGES AND CT SCAN."
[23] D. Karthikeyan, A. S. Varde, and W. Wang, "Transfer learning for decision support in Covid-19 detection from a few images in big data," in 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 4873-4881: IEEE.
[24] D. Yang, C. Martinez, L. Visuña, H. Khandhar, C. Bhatt, and J. J. S. R. Carretero, "Detection and analysis of COVID-19 in medical images using deep learning techniques," vol. 11, no. 1, pp. 1-13, 2021.
[25] E. H. Lee et al., "Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT," vol. 4, no. 1, pp. 1-11, 2021.
[26] F. O. Catak and K. Şahinbaş, "Human-in-the-Loop Enhanced COVID-19 Detection in Transfer Learning-Based CNN Models," in Computational Intelligence for COVID-19 and Future Pandemics: Springer, 2022, pp. 71-87.
[27] N. Sharma, L. Saba, N. N. Khanna, M. K. Kalra, M. M. Fouda, and J. S. J. D. Suri, "Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans," vol. 12, no. 9, p. 2132, 2022.
[28] C. Jin et al., "Development and evaluation of an artificial intelligence system for COVID-19 diagnosis," vol. 11, no. 1, pp. 1-14, 2020.
[29] F. Pan et al., "A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)," vol. 11, no. 1, pp. 1-11, 2021.
[30] S. Jadon, "COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach," in Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 2021, vol. 11601, pp. 161-170: SPIE.
[31] M. J. Horry et al., "X-ray image based COVID-19 detection using pre-trained deep learning models," 2020.
[32] A. Patil, "Covid Classification Using Audio Data," International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 9, no. X, 2021.
[33] K. Mahmoud et al., "Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models," vol. 10, no. 1, p. 35, 2021.
[34] D. R. Kandati and T. R. J. E. Gadekallu, "Genetic clustered federated learning for COVID-19 detection," vol. 11, no. 17, p. 2714, 2022.
[2] F. M. Shah et al., "A comprehensive survey of covid-19 detection using medical images," vol. 2, no. 6, p. 434, 2021.
[3] W. h. organization, "Listings of WHO’s response to COVID-19," https://www.who.int/news/item/29-06-2020-covidtimeline, 2020.
[4] A. M. Kadim, F. S. Al-Mukhtar, N. A. Hasan, A. B. Alnajjar, M. S. M. J. P. o. E. Altaei, and N. Sciences, "K-Means clustering of optimized wireless network sensor using genetic algorithm," vol. 10, no. 3, pp. 276-285, 2022.
[5] J. Shen et al., "A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine," vol. 5, 2022.
[6] E. I. F. B. I. RESEARCH, "COVID-19 imaging datasets, Overview of open access COVID-19 imaging datasets for teaching, training and research," https://www.eibir.org/covid-19-imaging-datasets/, 2021.
[7] A. H. T. Al-Ghrairi, A. A. Mohammed, and E. Z. J. I. I. J. o. A. I. Sameen, "Face detection and recognition with 180 degree rotation based on principal component analysis algorithm," vol. 11, no. 2, p. 593, 2022.
[8] M. Abdul Salam, S. Taha, and M. J. P. O. Ramadan, "COVID-19 detection using federated machine learning," vol. 16, no. 6, p. e0252573, 2021.
[9] S. Mandal, S. Mallick, A. Roy, and A. J. I. J. o. C. A. Paul, "AI based Automated Diagnosis of COVID-19 Patients," vol. 975, p. 8887.
[10] R. Harkness, G. Hall, A. F. Frangi, N. Ravikumar, and K. J. a. p. a. Zucker, "The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays," 2021.
[11] R. Qu, Y. Yang, and Y. Wang, "COVID-19 detection using CT image based on YOLOv5 network," in 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), 2021, pp. 622-625: IEEE.
[12] L. R. Baltazar et al., "Artificial intelligence on COVID-19 pneumonia detection using chest xray images," vol. 16, no. 10, p. e0257884, 2021.
[13] M. K. Hasan et al., "Challenges of deep learning methods for COVID-19 detection using public datasets," vol. 30, p. 100945, 2022.
[14] A. M. Ismael and A. J. E. S. w. A. Şengür, "Deep learning approaches for COVID-19 detection based on chest X-ray images," vol. 164, p. 114054, 2021.
[15] S. Mishra, H. K. Shukla, R. Singh, V. Pandey, S. Sagar, and Y. Singh, "Covid-19 Detection Using AI," 2021.
[16] M. Mahbub, M. Biswas, A. M. Miah, A. Shahabaz, and M. S. Kaiser, "Covid-19 detection using chest x-ray images with a regnet structured deep learning model," in International Conference on Applied Intelligence and Informatics, 2021, pp. 358-370: Springer.
[17] M. Mohamed, D. Hamam, and S. Ahmed, "COVID-19 Detection from Chest X-ray Images Using Artificial-Intelligence-Based Model Imported in a Mobile Application," 2021.
[18] S. Ali, A. Hussain, S. Bhattacharjee, A. Athar, and H.-C. J. S. Kim, "Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model," vol. 22, no. 24, p. 9983, 2022.
[19] V. J. T. Gupta, "COVID-19 Detection using Deep Learning," vol. 6, no. 12, pp. 421-425, 2020.
[20] J. Luo, Y. Sun, J. Chi, X. Liao, C. J. B. M. I. Xu, and D. Making, "A novel deep learning-based method for COVID-19 pneumonia detection from CT images," vol. 22, no. 1, pp. 1-7, 2022.
[21] S. Mahajan, A. Raina, X. Z. Gao, A. K. J. C. Pandit, C. Practice, and Experience, "COVID‐19 detection using hybrid deep learning model in chest x‐rays images," vol. 34, no. 5, p. e6747, 2022.
[22] E. Sankar, M. Y. Varma, and K. A. Kumar, "COVID-19 DETECTION USING CHEST X-RAY IMAGES AND CT SCAN."
[23] D. Karthikeyan, A. S. Varde, and W. Wang, "Transfer learning for decision support in Covid-19 detection from a few images in big data," in 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 4873-4881: IEEE.
[24] D. Yang, C. Martinez, L. Visuña, H. Khandhar, C. Bhatt, and J. J. S. R. Carretero, "Detection and analysis of COVID-19 in medical images using deep learning techniques," vol. 11, no. 1, pp. 1-13, 2021.
[25] E. H. Lee et al., "Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT," vol. 4, no. 1, pp. 1-11, 2021.
[26] F. O. Catak and K. Şahinbaş, "Human-in-the-Loop Enhanced COVID-19 Detection in Transfer Learning-Based CNN Models," in Computational Intelligence for COVID-19 and Future Pandemics: Springer, 2022, pp. 71-87.
[27] N. Sharma, L. Saba, N. N. Khanna, M. K. Kalra, M. M. Fouda, and J. S. J. D. Suri, "Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans," vol. 12, no. 9, p. 2132, 2022.
[28] C. Jin et al., "Development and evaluation of an artificial intelligence system for COVID-19 diagnosis," vol. 11, no. 1, pp. 1-14, 2020.
[29] F. Pan et al., "A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)," vol. 11, no. 1, pp. 1-11, 2021.
[30] S. Jadon, "COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach," in Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 2021, vol. 11601, pp. 161-170: SPIE.
[31] M. J. Horry et al., "X-ray image based COVID-19 detection using pre-trained deep learning models," 2020.
[32] A. Patil, "Covid Classification Using Audio Data," International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 9, no. X, 2021.
[33] K. Mahmoud et al., "Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models," vol. 10, no. 1, p. 35, 2021.
[34] D. R. Kandati and T. R. J. E. Gadekallu, "Genetic clustered federated learning for COVID-19 detection," vol. 11, no. 17, p. 2714, 2022.
Downloads
Published
2023-09-30
How to Cite
Kadim, A. M., Saad Al-Mukhtar, F. S., & Mahdi Altaei, M. S. (2023). Medical Images Based Covid-19 Detection Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(3), Comp Page 129–140. https://doi.org/10.29304/jqcm.2023.15.3.1270
Issue
Section
Computer Articles
