Abstract
In March 2020, the World Health Organisation (WHO) identified COVID-19 as a global pandemic brought about by the SARS-CoV-2 virus. RT-PCR continued to be the first line of diagnosis, but its slow turnaround emphasized the need for faster, complementary diagnostic modalities. Imaging techniques like chest X-rays (CXR), computed tomography (CT) scans, and ultrasound were soon in high demand; yet manual assessment was time-consuming and susceptible to faults. To overcome these challenges, artificial intelligence (AI), and more so deep learning (DL), has proven to be a revolutionary instrument through feature extraction automation and enhanced diagnostic accuracy. This survey differentiates itself from reviews that have already been conducted by providing an exhaustive review of state-of-the-art DL architectures engineered for COVID-19 analysis on various imaging modalities, also highlighting under-investigated aspects like prognostics, rehabilitation assistance, and the role played by uncertainty quantification (UQ) in achieving clinical trustworthiness. The survey identifies areas of research gaps, such as the scarcity of multimodal datasets, difficulties in generalising models across populations, and the absence of standardised evaluation benchmarks. Systematically resolving these gaps, this work highlights the practical significance of AI-based computer-aided diagnosis (CAD) systems toward accelerating faster, more robust, and scalable pandemic response tools. In addition, it gives researchers and clinicians a blueprint for developing AI-based healthcare, allowing for both short-term use in managing COVID-19 and long-term relevance in future public health emergencies.
References
Ibrahim, Nahla Khamis. "Epidemiologic surveillance for controlling Covid-19 pandemic: types, challenges and implications." Journal of infection and public health 13, no. 11 (2020): 1630-1638.
Albahri, O. S., A. A. Zaidan, A. S. Albahri, B. B. Zaidan, Karrar Hameed Abdulkareem, Z. T. Al-Qaysi, A. H. Alamoodi et al. "Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects." Journal of infection and public health 13, no. 10 (2020): 1381-1396.
Aggarwal, Priya, Narendra Kumar Mishra, Binish Fatimah, Pushpendra Singh, Anubha Gupta, and Shiv Dutt Joshi. "COVID-19 image classification using deep learning: Advances, challenges and opportunities." Computers in Biology and Medicine 144 (2022): 105350.
Islam, Muhammad Nazrul, Toki Tahmid Inan, Suzzana Rafi, Syeda Sabrina Akter, Iqbal H. Sarker, and AKM Najmul Islam. "A systematic review on the use of AI and ML for fighting the COVID-19 pandemic." IEEE Transactions on Artificial Intelligence 1, no. 3 (2021): 258-270.
Chen, Joy Iong-Zong. "Design of accurate classification of COVID-19 disease in X-ray images using deep learning approach." Journal of ISMAC 3, no. 02 (2021): 132-148.
Soares, Eduardo, Plamen Angelov, Sarah Biaso, Michele Higa Froes, and Daniel Kanda Abe. "SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification." MedRxiv (2020): 2020-04.
Zhu, Ziwei, Zhang Xingming, Guihua Tao, Tingting Dan, Jiao Li, Xijie Chen, Yang Li et al. "Classification of COVID-19 by compressed chest CT image through deep learning on a large patients cohort." Interdisciplinary Sciences: Computational Life Sciences 13, no. 1 (2021): 73-82.
Zhang, Li, Eric A. Hoffman, and Joseph M. Reinhardt. "Atlas-driven lung lobe segmentation in volumetric X-ray CT images." IEEE transactions on medical imaging 25, no. 1 (2006): 1-16.
Khan, Wasif, Nazar Zaki, and Luqman Ali. "Intelligent pneumonia identification from chest x-rays: A systematic literature review." IEEE Access 9 (2021): 51747-51771.
Rahmani, Amir Masoud, Elham Azhir, Morteza Naserbakht, Mokhtar Mohammadi, Adil Hussein Mohammed Aldalwie, Mohammed Kamal Majeed, Sarkhel H. Taher Karim, and Mehdi Hosseinzadeh. "Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review." Multimedia tools and applications 81, no. 20 (2022): 28779-28798.
Yao, Dengfeng, Wanle Chi, and Mohammad Khishe. "Parkinson’s disease and cleft lip and palate of pathological speech diagnosis using deep convolutional neural networks evolved by IPWOA." Applied Acoustics 199 (2022): 109003.
Wong, Pak Kin, Tao Yan, Huaqiao Wang, In Neng Chan, Jiangtao Wang, Yang Li, Hao Ren, and Chi Hong Wong. "Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network." Biomedical Signal Processing and Control 73 (2022): 103415.
de-Torres, Juan P., David O. Wilson, Pablo Sanchez-Salcedo, Joel L. Weissfeld, Juan Berto, Arantzazu Campo, Ana B. Alcaide, Marta García-Granero, Bartolome R. Celli, and Javier J. Zulueta. "Lung cancer in patients with chronic obstructive pulmonary disease. Development and validation of the COPD Lung Cancer Screening Score." American journal of respiratory and critical care medicine 191, no. 3 (2015): 285-291.
Gatidis, Sergios, Tobias Hepp, Marcel Früh, Christian La Fougère, Konstantin Nikolaou, Christina Pfannenberg, Bernhard Schölkopf, Thomas Küstner, Clemens Cyran, and Daniel Rubin. "A whole-body FDG-PET/CT dataset with manually annotated tumor lesions." Scientific Data 9, no. 1 (2022): 601
Ghashghaei, Sara, David A. Wood, Erfan Sadatshojaei, and Mansooreh Jalilpoor. "Grayscale image statistical attributes effectively distinguish the severity of lung abnormalities in ct scan slices of covid-19 patients." SN Computer Science 4, no. 2 (2023): 201.
Nur, A., Md Saikat Islam Khan, and Mostofa Kamal Nasir. "Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images." Intelligent Systems with Applications 17 (2023): 200182.
Sailunaz, Kashfia, Tansel Özyer, Jon Rokne, and Reda Alhajj. "A survey of machine learning-based methods for COVID-19 medical image analysis." Medical & Biological Engineering & Computing 61, no. 6 (2023): 1257-1297.
Dairi, Abdelkader, Fouzi Harrou, Abdelhafid Zeroual, Mohamad Mazen Hittawe, and Ying Sun. "Comparative study of machine learning methods for COVID-19 transmission forecasting." Journal of biomedical informatics 118 (2021): 103791.
Tartaglione, Enzo, Carlo Alberto Barbano, Claudio Berzovini, Marco Calandri, and Marco Grangetto. "Unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data." International Journal of Environmental Research and Public Health 17, no. 18 (2020): 6933.
Constantinou, Marios, Themis Exarchos, Aristidis G. Vrahatis, and Panagiotis Vlamos. "COVID-19 classification on chest X-ray images using deep learning methods." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 2035.
Aslani, S., and J. Jacob. "Utilisation of deep learning for COVID-19 diagnosis." Clinical Radiology 78, no. 2 (2023): 150-157.
Gupta, Kapil, and Varun Bajaj. "Deep learning models-based CT-scan image classification for automated screening of COVID-19." Biomedical Signal Processing and Control 80 (2023): 104268.
Cenggoro, Tjeng Wawan, and Bens Pardamean. "A systematic literature review of machine learning application in COVID-19 medical image classification." Procedia computer science 216 (2023): 749-756.
Bhosale, Yogesh H., and K. Sridhar Patnaik. "Application of deep learning techniques in diagnosis of covid-19 (coronavirus): a systematic review." Neural processing letters 55, no. 3 (2023): 3551-3603.
Abraham, Tara H. "(Physio) logical circuits: The intellectual origins of the McCulloch–Pitts neural networks." Journal of the History of the Behavioral Sciences 38, no. 1 (2002): 3-25.
Chen, Jun, Lianlian Wu, Jun Zhang, Liang Zhang, Dexin Gong, Yilin Zhao, Qiuxiang Chen et al. "Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography." Scientific reports 10, no. 1 (2020): 19196.
Kwekha-Rashid, Ameer Sardar, Heamn N. Abduljabbar, and Bilal Alhayani. "Coronavirus disease (COVID-19) cases analysis using machine-learning applications." Applied nanoscience 13, no. 3 (2023): 2013-2025.
Blbas, Hazhar TA, and Shahen M. Faraj. "A statistical study of the influence of COVID-19 on the agricultural supply chain (vegetative) production in Halabja governorate." Cihan University-Erbil Scientific Journal 6, no. 1 (2022): 1-6.
Khanday, Akib Mohi Ud Din, Syed Tanzeel Rabani, Qamar Rayees Khan, Nusrat Rouf, and Masarat Mohi Ud Din. "Machine learning based approaches for detecting COVID-19 using clinical text data." International Journal of Information Technology 12, no. 3 (2020): 731-739.
Pham, Tuan D. "Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?." Health Information Science and Systems 9, no. 1 (2020): 2.
Zivkovic, Miodrag, Nebojsa Bacanin, K. Venkatachalam, Anand Nayyar, Aleksandar Djordjevic, Ivana Strumberger, and Fadi Al-Turjman. "COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach." Sustainable cities and society 66 (2021): 102669.
Amyar, Amine, Romain Modzelewski, Hua Li, and Su Ruan. "Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation." Computers in biology and medicine 126 (2020): 104037.
Salama, Wessam M., and Moustafa H. Aly. "Framework for COVID-19 segmentation and classification based on deep learning of computed tomography lung images." Journal of Electronic Science and Technology 20, no. 3 (2022): 100161.
Hussain, Emtiaz, Mahmudul Hasan, Md Anisur Rahman, Ickjai Lee, Tasmi Tamanna, and Mohammad Zavid Parvez. "CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images." Chaos, Solitons & Fractals 142 (2021): 110495.
Silva, Pedro, Eduardo Luz, Guilherme Silva, Gladston Moreira, Rodrigo Silva, Diego Lucio, and David Menotti. "COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis." Informatics in medicine unlocked 20 (2020): 100427.
Vidal, Plácido L., Joaquim de Moura, Jorge Novo, and Marcos Ortega. "Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19." Expert Systems with Applications 173 (2021): 114677.
Agarwal, Mohit, Luca Saba, Suneet K. Gupta, Alessandro Carriero, Zeno Falaschi, Alessio Paschè, Pietro Danna, Ayman El-Baz, Subbaram Naidu, and Jasjit S. Suri. "A novel block imaging technique using nine artificial intelligence models for COVID-19 disease classification, characterization and severity measurement in lung computed tomography scans on an Italian cohort." Journal of Medical Systems 45, no. 3 (2021): 28.
Kordnoori, Shirin, Malihe Sabeti, Hamidreza Mostafaei, and Saeed Seyed Agha Banihashemi. "Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease." IET Image Processing 17, no. 5 (2023): 1534-1545.
Irmak, Emrah. "COVID‐19 disease severity assessment using CNN model." IET image processing 15, no. 8 (2021): 1814-1824.
Mohagheghi, Saeed, Mehdi Alizadeh, Seyed Mahdi Safavi, Amir Hossein Foruzan, and Yen-Wei Chen. "Integration of CNN, CBMIR, and visualization techniques for diagnosis and quantification of covid-19 disease." IEEE Journal of Biomedical and Health Informatics 25, no. 6 (2021): 1873-1880.
Sakib, Sadman, Tahrat Tazrin, Mostafa M. Fouda, Zubair Md Fadlullah, and Mohsen Guizani. "DL-CRC: deep learning-based chest radiograph classification for COVID-19 detection: a novel approach." Ieee Access 8 (2020): 171575-171589.
Wang, Siwen, Di Dong, Liang Li, Hailin Li, Yan Bai, Yahua Hu, Yuanyi Huang et al. "A deep learning radiomics model to identify poor outcome in COVID-19 patients with underlying health conditions: a multicenter study." IEEE Journal of Biomedical and Health Informatics 25, no. 7 (2021): 2353-2362.
Lin, Li, Qin Lixin, and Xu Zeguo. "Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT." Radiology 296, no. 2 (2020).
Ibrahim, Abdullahi Umar, Mehmet Ozsoz, Sertan Serte, Fadi Al-Turjman, and Polycarp Shizawaliyi Yakoi. "Pneumonia classification using deep learning from chest X-ray images during COVID-19." Cognitive computation 16, no. 4 (2024): 1589-1601.
Padma, T., and Ch Usha Kumari. "Deep learning based chest x-ray image as a diagnostic tool for covid-19." In 2020 international conference on smart electronics and communication (ICOSEC), pp. 589-592. IEEE, 2020.
Xu, Jingxiang, Jianqiang Li, Juan Li, Linna Zhao, and Shujie Ding. "Covid-IRLNet: A COVID-19 Diagnostic Model For Extracting CT Image Features and CT Sequence Features." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, (2024): 2159-2164.
Zhan, Guilin, Kai Qian, Wenyang Chen, Dandan Xue, Mengdi Li, Jun Zhang, and Yonghang Tai. "EAswin-unet: segmenting CT images of COVID-19 with edge-fusion attention." Biomedical Signal Processing and Control 89 (2024):
Balaha, Hossam Magdy, Mayada Elgendy, Ahmed Alksas, Mohamed Shehata, Norah Saleh Alghamdi, Fatma Taher, Mohammed Ghazal et al. "A Neuroimaging Yolov8-Based Cad Framework for Anosmia Grading in Covid-19." In 2024 IEEE International Conference on Image Processing (ICIP), IEEE, (2024): 2951-2956.
Moosavi, Abdoulreza S., Ashraf Mahboobi, Farzin Arabzadeh, Nazanin Ramezani, Helia S. Moosavi, and Golbarg Mehrpoor. "Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model." Journal of Family Medicine and Primary Care 13, no. 2 (2024): 691-698.
Tembhurne, Jitendra. "Classification of COVID-19 patients from HRCT score prediction in CT images using transfer learning approach." Journal of Electrical Systems and Information Technology 11, no. 1 (2024): 4.
Liu, Weili, Bo Wang, Yucheng Song, and Zhifang Liao. "Radiological image analysis using effective channel extension and fusion network based on COVID CT images." Journal of Radiation Research and Applied Sciences 17, no. 3 (2024): 100965.
Punitha, S., Thompson Stephan, Ramani Kannan, Mufti Mahmud, M. Shamim Kaiser, and Samir Brahim Belhaouari. "Detecting COVID-19 from lung computed tomography images: A swarm optimized artificial neural network approach." IEEE Access 11 (2023): 12378-12393.
Xu, Yujia, Hak-Keung Lam, Guangyu Jia, Jian Jiang, Junkai Liao, and Xinqi Bao. "Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation." Computers in Biology and Medicine 152 (2023): 106417.
Afif, Mouna, Riadh Ayachi, Yahia Said, and Mohamed Atri. "Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction." Multimedia Tools and Applications 82, no. 17 (2023): 26885-26899.
Wu, Xing, Cheng Chen, Mingyu Zhong, Jianjia Wang, and Jun Shi. "COVID-AL: The diagnosis of COVID-19 with deep active learning." Medical Image Analysis 68 (2021): 101913.
Deng, Yan, Lei Lei, Yue Chen, and Wei Zhang. "The potential added value of FDG PET/CT for COVID-19 pneumonia." European journal of nuclear medicine and molecular imaging 47, no. 7 (2020): 1634-1635.
Apostolopoulos, Ioannis D., and Tzani A. Mpesiana. "Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks." Physical and engineering sciences in medicine 43, no. 2 (2020): 635-640.
Shankar, K., and Eswaran Perumal. "A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images." Complex & Intelligent Systems 7, no. 3 (2021): 1277-1293.
Shin, Hoo-Chang, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M. Summers. "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning." IEEE transactions on medical imaging 35, no. 5 (2016): 1285-1298.
Gao, Xiaohong W., Carl James-Reynolds, and Edward Currie. "Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture." Neurocomputing 392 (2020): 233-244.
Nardelli, Pietro, Daniel Jimenez-Carretero, David Bermejo-Pelaez, George R. Washko, Farbod N. Rahaghi, Maria J. Ledesma-Carbayo, and Raúl San José Estépar. "Pulmonary artery–vein classification in CT images using deep learning." IEEE transactions on medical imaging 37, no. 11 (2018): 2428-2440.
Wang, Shuai, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai et al. "A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)." European radiology 31, no. 8 (2021): 6096-6104.
Ouchicha, Chaimae, Ouafae Ammor, and Mohammed Meknassi. "CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images." Chaos, Solitons & Fractals 140 (2020): 110245.
Javaheri, Tahereh, Morteza Homayounfar, Zohreh Amoozgar, Reza Reiazi, Fatemeh Homayounieh, Engy Abbas, Azadeh Laali et al. "CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images." NPJ digital medicine 4, no. 1 (2021): 29.
Saad, Waleed, Wafaa A. Shalaby, Mona Shokair, Fathi Abd El-Samie, Moawad Dessouky, and Essam Abdellatef. "COVID-19 classification using deep feature concatenation technique." Journal of Ambient Intelligence and Humanized Computing 13, no. 4 (2022): 2025-2043.
Singh, Dilbag, Vijay Kumar, and Manjit Kaur. "Densely connected convolutional networks-based COVID-19 screening model." Applied Intelligence 51, no. 5 (2021): 3044-3051.
Pathak, Yadunath, Piyush Kumar Shukla, and K. V. Arya. "Deep bidirectional classification model for COVID-19 disease infected patients." IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, no. 4 (2020): 1234-1241.
Singh, Dilbag, Vijay Kumar, Vaishali, and Manjit Kaur. "Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks." European Journal of Clinical Microbiology & Infectious Diseases 39, no. 7 (2020): 1379-1389.
Li, Lanjuan, Haiyang Huang, and Xinyu Jin. "AE-CNN classification of pulmonary tuberculosis based on CT images." In 2018 9th international conference on information technology in medicine and education (ITME), IEEE, (2018): 39-42.
Baghdadi, Nadiah A., Amer Malki, Sally F. Abdelaliem, Hossam Magdy Balaha, Mahmoud Badawy, and Mostafa Elhosseini. "An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network." Computers in biology and medicine 144 (2022): 105383.
Kundu, Rohit, Pawan Kumar Singh, Seyedali Mirjalili, and Ram Sarkar. "COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble." Computers in Biology and Medicine 138 (2021): 104895.
Nneji, Grace Ugochi, Jianhua Deng, Happy Nkanta Monday, Md Altab Hossin, Sandra Obiora, Saifun Nahar, and Jingye Cai. "Covid-19 identification from low-quality computed tomography using a modified enhanced super-resolution generative adversarial network plus and siamese capsule network." In Healthcare, vol. 10, no. 2, p. 403. MDPI, 2022.
Khan, Asif Iqbal, Junaid Latief Shah, and Mohammad Mudasir Bhat. "CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images." Computer methods and programs in biomedicine 196 (2020): 105581. https://doi.org/10.1016/j.cmpb.2020.105581
Yu, Zekuan, Xiaohu Li, Haitao Sun, Jian Wang, Tongtong Zhao, Hongyi Chen, Yichuan Ma, Shujin Zhu, and Zongyu Xie. "Rapid identification of COVID-19 severity in CT scans through classification of deep features." Biomedical engineering online 19, no. 1 (2020): 63.
Vadduri, Maneesha, and P. Kuppusamy. "Enhancing ocular healthcare: deep learning-based multi-class diabetic eye disease segmentation and classification." IEEe Access 11 (2023): 137881-137898.
Kuppusamy, P., P. Harshitha, and M. Dhanyasri. "Customized CNN with Adam and Nadam optimizers for emotion recognition using facial expressions." In 2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), IEEE, (2023): 1-5.