A Review on Covid-19 Detection Using Artificial Intelligence from Chest CT Scan Slices
Abstract The outbreak of COVID-19, a contagious respiratory disease, has had a significant impact on people worldwide. To prevent its spread, there is an urgent need for an easily accessible, fast, and cost-effective diagnostic solution. According to studies, COVID-19 is frequently accompanied by coughing. Therefore, the identification and classification of cough sounds can be a promising method for rapidly and efficiently diagnosing the disease. The COVID-19 epidemic has resulted in a worldwide health crisis, and stopping the disease's spread depends on a quick and precise disease diagnosis. COVID-19 has been detected using medical imaging modalities such as chest X-rays and computed tomography (CT) scans due to their non-invasive nature and accessibility. This research provides an in-depth examination of deep learning-based strategies for recognising COVID-19 in medical images. The benefits and drawbacks of various deep learning approaches and their applications in COVID-19 detection are discussed. The study also examines publicly available datasets and benchmarks for evaluating deep learning model performance. Furthermore, the limitations and future research prospects for using deep learning in COVID-19 detection are discussed. This survey's goal is to offer a comprehensive overview of the current state of advancement in deep learning-based COVID-19 detection using medical images. This can aid researchers and healthcare professionals in selecting appropriate approaches for an effective diagnosis of the disease.
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Bekhet, S., Alkinani, M. H., Tabares-Soto, R., & Hassaballah, M. (2021). An efficient method for covid-19 detection using light weight convolutional neural network. Computers, Materials and Continua, 69(2), 2475–2491. 10.32604/cmc.2021.018514
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Foysal Haque, K., Farhan Haque, F., Gandy, L., & Abdelgawad, A. (2020). Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks. Proceedings - 2020 International Conference on Computing, Electronics and Communications Engineering, ICCECE 2020, 125–130. 10.1109/iCCECE49321.2020.9231235
Halder, A., & Datta, B. (2021). COVID-19 detection from lung CT-scan images using transfer learning approach. Machine Learning: Science and Technology, 2(4). 10.1088/2632-2153/abf22c
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020). COVID-19 Detection through Transfer Learning Using Multimodal Imaging Data. IEEE Access, 8, 149808–149824. 10.1109/ACCESS.2020.3016780
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Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. (2021). Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach. Biocybernetics and Biomedical Engineering, 41(3), 867–879. 10.1016/j.bbe.2021.05.013
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Kieu, S. T. H., Bade, A., Hijazi, M. H. A., & Kolivand, H. (2021). COVID-19 Detection Using Integration of Deep Learning Classifiers and Contrast-Enhanced Canny Edge Detected X-Ray Images. IT Professional, 23(4), 51–56. 10.1109/MITP.2021.3052205
Laguarta, J., Hueto, F., & Subirana, B. (2020). COVID-19 Artificial Intelligence Diagnosis Using only Cough Recordings. IEEE Open Journal of Engineering in Medicine and Biology, 1, 275–281. 10.1109/OJEMB.2020.3026928
Liu, J., Zhang, Z., Zu, L., Wang, H., & Zhong, Y. (2020). Intelligent Detection for CT Image of COVID-19 using Deep Learning. Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020, 76–81. 10.1109/CISP-BMEI51763.2020.9263690
Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020). CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Computers in Biology and Medicine, 122. 10.1016/j.compbiomed.2020.103869
Meng, Z., Wang, M., Song, H., Guo, S., Zhou, Y. Y. Y., Li, W., Zhou, Y. Y. Y., Li, M., Song, X., Zhou, Y. Y. Y., Li, Q., Lu, X., & Ying, B. (2020). Development and utilization of an intelligent application for aiding COVID-19 diagnosis. MedRxiv, 37, 2020.03.18.20035816. https://www.medrxiv.org/content/10.1101/2020.03.18.20035816v1
Mishra, M., Parashar, V., & Shimpi, R. (2020). Development and evaluation of an AI System for early detection of Covid-19 pneumonia using X-ray (Student Consortium). Proceedings - 2020 IEEE 6th International Conference on Multimedia Big Data, BigMM 2020, 292–296. 10.1109/BigMM50055.2020.00051
Mishra, N. K., Singh, P., & Joshi, S. D. (2021). Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybernetics and Biomedical Engineering, 41(2), 572–588. 10.1016/j.bbe.2021.04.006
Nair, R., Vishwakarma, S., Soni, M., Patel, T., & Joshi, S. (2022). Detection of COVID-19 cases through X-ray images using hybrid deep neural network. World Journal of Engineering, 19(1), 33–39. 10.1108/WJE-10-2020-0529
Ouchicha, C., Ammor, O., & Meknassi, M. (2020). CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images. Chaos, Solitons and Fractals, 140. 10.1016/j.chaos.2020.110245
Ozturk, T., Talo, M., Azra, E., Baran, U., & Yildirim, O. (2020). Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information. Computers in Biology and Medicine, January.
Patgar, C. C., Patil, D. D., Rahate, S. H., & Randive, S. (2022). Covid-19 Detection using Deep Learning. 2022 International Conference on Signal and Information Processing, IConSIP 2022. 10.1109/ICoNSIP49665.2022.10007465
Pont-Tuset, J., & Marques, F. (2016). Supervised Evaluation of Image Segmentation and Object Proposal Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7), 1465–1478. 10.1109/TPAMI.2015.2481406
Sangidong, J. C., Purnomo, H. D., & Santoso, F. Y. (2021). Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, 61–65. 10.1109/EIConCIT50028.2021.9431887
Shadin, N. S., Sanjana, S., & Lisa, N. J. (2021). COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(CNN) and InceptionV3. 2021 International Conference on Information Technology, ICIT 2021 - Proceedings 3 (September 2012), 799–804. 10.1109/ICIT52682.2021.9491752
Shah, F. M., Joy, S. K. S., Ahmed, F., Hossain, T., Humaira, M., Ami, A. S., Paul, S., Jim, M. A. R. K., & Ahmed, S. (2021). A Comprehensive Survey of COVID-19 Detection Using Medical Images. SN Computer Science, 2(6), 1–22. 10.1007/s42979-021-00823-1
Sharma, S., & Tiwari, S. (2021). COVID-19 Diagnosis using X-Ray Images and Deep learning. Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, 344–349. 10.1109/ICAIS50930.2021.9395851
Shrivastava, P., Singh, A., Agarwal, S., Tekchandani, H., & Verma, S. (2021). Covid detection in CT and X-Ray images using Ensemble Learning. Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Iccmc, 1085–1090. 10.1109/ICCMC51019.2021.9418308
Singh, M., Bansal, S., Ahuja, S., Dubey, R. K., Panigrahi, B. K., & Dey, N. (2021). Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data. Medical and Biological Engineering and Computing, 59(4), 825–839. 10.1007/s11517-020-02299-2
Toraman, S., Alakus, T. B., & Turkoglu, I. (2020). Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons and Fractals, 140. 10.1016/j.chaos.2020.110122
Wang, T., Zhao, Y., Zhu, L., Liu, G., Ma, Z., & Zheng, J. (2020). Lung CT image aided detection COVID-19 based on Alexnet network. Proceedings - 2020 5th International Conference on Communication, Image and Signal Processing, CCISP 2020, 199–203. 10.1109/CCISP51026.2020.9273512
Weng, W., & Zhu, X. (2021). INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access, 9, 16591–16603. 10.1109/ACCESS.2021.3053408
Yang, D., Martinez, C., Visuña, L., Khandhar, H., Bhatt, C., & Carretero, J. (2021). Detection and analysis of COVID-19 in medical images using deep learning techniques. Scientific Reports, 11(1), 1–13. 10.1038/s41598-021-99015-3
Zhao, W., Jiang, W., & Qiu, X. (2021). Deep learning for COVID-19 detection based on CT images. Scientific Reports, 11(1), 1–12. 10.1038/s41598-021-93832-2
Adeshina, S. A. (2022). A deep Learning Based methodology.
Agrawal, T., & Choudhary, P. (2022). FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images. Evolving Systems, 13(4), 519–533. 10.1007/s12530-021-09385-2
Akter, S., Shamrat, F. M. J. M., Chakraborty, S., Karim, A., & Azam, S. (2021). Covid-19 detection using deep learning algorithm on chest X-ray images. Biology, 10(11). 10.3390/biology10111174
Alasasfeh, H. O., Alomari, T., & Ibbini, M. S. (2021). Deep Learning Approach for COVID-19 Detection Based on X-Ray Images. 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, January, 601–606. 10.1109/SSD52085.2021.9429383
Alazab, M., Awajan, A., Mesleh, A., Abraham, A., Jatana, V., & Alhyari, S. (2020). COVID-19 Prediction and Detection Using Deep Learning. In International Journal of Computer Information Systems and Industrial Management Applications (Vol. 12). www.mirlabs.net/ijcisim/index.html
Alqudah, A. M., Qazan, S., & Alqudah, A. (2020). Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks. Emergency Radiology, 4(1), 54–67. https://www.researchsquare.com/article/rs-24305/latest.pdf
Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121. 10.1016/j.compbiomed.2020.103795
Asghar, U., Arif, M., Ejaz, K., Vicoveanu, D., Izdrui, D., & Geman, O. (2022). An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification. BioMed Research International, 2022. 10.1155/2022/8925930
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). \href{https://arxiv.org/pdf/1511.00561.pdf}{Segnet: A deep convolutional encoder-decoder architecture for image segmentation}. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://arxiv.org/pdf/1511.00561.pdf
Bekhet, S., Alkinani, M. H., Tabares-Soto, R., & Hassaballah, M. (2021). An efficient method for covid-19 detection using light weight convolutional neural network. Computers, Materials and Continua, 69(2), 2475–2491. 10.32604/cmc.2021.018514
Bekhet, S., Hassaballah, M., Kenk, M. A., & Hameed, M. A. (2020). An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray. 2nd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2020, 191–195. 10.1109/NILES50944.2020.9257930
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., Jacobi, A., Li, K., Li, S., & Shan, H. (2020). CT imaging features of 2019 novel coronavirus (2019-NCoV). Radiology, 295(1), 202–207. 10.1148/radiol.2020200230
Das, A. K., Ghosh, S., Thunder, S., Dutta, R., Agarwal, S., & Chakrabarti, A. (2021). Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Analysis and Applications, 24(3), 1111–1124. 10.1007/s10044-021-00970-4
Das, D., Biswas, S. K., & Bandyopadhyay, S. (2022). Perspective of AI system for COVID-19 detection using chest images: a review. Multimedia Tools and Applications, 81(15), 21471–21501. 10.1007/s11042-022-11913-4
Dhebe, R., Jagtap, V., Munde, P., & Salian, P. S. (2019). Covid-19 Detection using X-ray. 3307, 147–153.
Duong, L. T., Nguyen, P. T., Iovino, L., Flammini, M., & Linh, L. T. (2020). Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images. MedRxiv, August, 2020.08.13.20173997. 10.1101/2020.08.13.20173997 %0Ahttps://www.medrxiv.org/content/10.1101/2020.08.13.20173997v1.abstract
Feng, K., He, F., Steinmann, J., & Demirkiran, I. (2021). Deep-learning based approach to identify covid-19. Conference Proceedings - IEEE SOUTHEASTCON, 2021-March, 17–20. 10.1109/SoutheastCon45413.2021.9401826
Foysal Haque, K., Farhan Haque, F., Gandy, L., & Abdelgawad, A. (2020). Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks. Proceedings - 2020 International Conference on Computing, Electronics and Communications Engineering, ICCECE 2020, 125–130. 10.1109/iCCECE49321.2020.9231235
Halder, A., & Datta, B. (2021). COVID-19 detection from lung CT-scan images using transfer learning approach. Machine Learning: Science and Technology, 2(4). 10.1088/2632-2153/abf22c
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020). COVID-19 Detection through Transfer Learning Using Multimodal Imaging Data. IEEE Access, 8, 149808–149824. 10.1109/ACCESS.2020.3016780
Islam, M. R., & Matin, A. (2020). Detection of COVID 19 from CT Image by the Novel LeNet-5 CNN Architecture. ICCIT 2020 - 23rd International Conference on Computer and Information Technology, Proceedings, 19–21. 10.1109/ICCIT51783.2020.9392723
Kaheel, H., Hussein, A., & Chehab, A. (2021). AI-Based Image Processing for COVID-19 Detection in Chest CT Scan Images. Frontiers in Communications and Networks, 2(August), 1–12. 10.3389/frcmn.2021.645040
Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. (2021). Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach. Biocybernetics and Biomedical Engineering, 41(3), 867–879. 10.1016/j.bbe.2021.05.013
Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196. 10.1016/j.cmpb.2020.105581
Khan, A., Younis, S., & Algethami, H. (2021). Covid-19 Identification using deep neural networks. 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021. 10.1109/WIDSTAIF52235.2021.9430219
Kieu, S. T. H., Bade, A., Hijazi, M. H. A., & Kolivand, H. (2021). COVID-19 Detection Using Integration of Deep Learning Classifiers and Contrast-Enhanced Canny Edge Detected X-Ray Images. IT Professional, 23(4), 51–56. 10.1109/MITP.2021.3052205
Laguarta, J., Hueto, F., & Subirana, B. (2020). COVID-19 Artificial Intelligence Diagnosis Using only Cough Recordings. IEEE Open Journal of Engineering in Medicine and Biology, 1, 275–281. 10.1109/OJEMB.2020.3026928
Liu, J., Zhang, Z., Zu, L., Wang, H., & Zhong, Y. (2020). Intelligent Detection for CT Image of COVID-19 using Deep Learning. Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020, 76–81. 10.1109/CISP-BMEI51763.2020.9263690
Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020). CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Computers in Biology and Medicine, 122. 10.1016/j.compbiomed.2020.103869
Meng, Z., Wang, M., Song, H., Guo, S., Zhou, Y. Y. Y., Li, W., Zhou, Y. Y. Y., Li, M., Song, X., Zhou, Y. Y. Y., Li, Q., Lu, X., & Ying, B. (2020). Development and utilization of an intelligent application for aiding COVID-19 diagnosis. MedRxiv, 37, 2020.03.18.20035816. https://www.medrxiv.org/content/10.1101/2020.03.18.20035816v1
Mishra, M., Parashar, V., & Shimpi, R. (2020). Development and evaluation of an AI System for early detection of Covid-19 pneumonia using X-ray (Student Consortium). Proceedings - 2020 IEEE 6th International Conference on Multimedia Big Data, BigMM 2020, 292–296. 10.1109/BigMM50055.2020.00051
Mishra, N. K., Singh, P., & Joshi, S. D. (2021). Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybernetics and Biomedical Engineering, 41(2), 572–588. 10.1016/j.bbe.2021.04.006
Nair, R., Vishwakarma, S., Soni, M., Patel, T., & Joshi, S. (2022). Detection of COVID-19 cases through X-ray images using hybrid deep neural network. World Journal of Engineering, 19(1), 33–39. 10.1108/WJE-10-2020-0529
Ouchicha, C., Ammor, O., & Meknassi, M. (2020). CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images. Chaos, Solitons and Fractals, 140. 10.1016/j.chaos.2020.110245
Ozturk, T., Talo, M., Azra, E., Baran, U., & Yildirim, O. (2020). Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information. Computers in Biology and Medicine, January.
Patgar, C. C., Patil, D. D., Rahate, S. H., & Randive, S. (2022). Covid-19 Detection using Deep Learning. 2022 International Conference on Signal and Information Processing, IConSIP 2022. 10.1109/ICoNSIP49665.2022.10007465
Pont-Tuset, J., & Marques, F. (2016). Supervised Evaluation of Image Segmentation and Object Proposal Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7), 1465–1478. 10.1109/TPAMI.2015.2481406
Sangidong, J. C., Purnomo, H. D., & Santoso, F. Y. (2021). Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, 61–65. 10.1109/EIConCIT50028.2021.9431887
Shadin, N. S., Sanjana, S., & Lisa, N. J. (2021). COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(CNN) and InceptionV3. 2021 International Conference on Information Technology, ICIT 2021 - Proceedings 3 (September 2012), 799–804. 10.1109/ICIT52682.2021.9491752
Shah, F. M., Joy, S. K. S., Ahmed, F., Hossain, T., Humaira, M., Ami, A. S., Paul, S., Jim, M. A. R. K., & Ahmed, S. (2021). A Comprehensive Survey of COVID-19 Detection Using Medical Images. SN Computer Science, 2(6), 1–22. 10.1007/s42979-021-00823-1
Sharma, S., & Tiwari, S. (2021). COVID-19 Diagnosis using X-Ray Images and Deep learning. Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, 344–349. 10.1109/ICAIS50930.2021.9395851
Shrivastava, P., Singh, A., Agarwal, S., Tekchandani, H., & Verma, S. (2021). Covid detection in CT and X-Ray images using Ensemble Learning. Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Iccmc, 1085–1090. 10.1109/ICCMC51019.2021.9418308
Singh, M., Bansal, S., Ahuja, S., Dubey, R. K., Panigrahi, B. K., & Dey, N. (2021). Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data. Medical and Biological Engineering and Computing, 59(4), 825–839. 10.1007/s11517-020-02299-2
Toraman, S., Alakus, T. B., & Turkoglu, I. (2020). Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons and Fractals, 140. 10.1016/j.chaos.2020.110122
Wang, T., Zhao, Y., Zhu, L., Liu, G., Ma, Z., & Zheng, J. (2020). Lung CT image aided detection COVID-19 based on Alexnet network. Proceedings - 2020 5th International Conference on Communication, Image and Signal Processing, CCISP 2020, 199–203. 10.1109/CCISP51026.2020.9273512
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