Abstract
Original language | English |
---|---|
Journal | SN Computer Science |
Volume | 3 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Artificial intelligence
- CNN
- COVID-19 detection
- COVID-19 diagnosis
- COVID-19 prediction
- Deep learning
- Machine learning
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Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey : SN Computer Science. / Meraihi, Y.; Gabis, A.B.; Mirjalili, S. et al.
In: SN Computer Science, Vol. 3, No. 4, 2022.Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey
T2 - SN Computer Science
AU - Meraihi, Y.
AU - Gabis, A.B.
AU - Mirjalili, S.
AU - Ramdane-Cherif, A.
AU - Alsaadi, F.E.
N1 - Export Date: 11 July 2022 Correspondence Address: Meraihi, Y.; LIST Laboratory, Avenue of Independence, Algeria; email: y.meraihi@univ-boumerdes.dz References: Lai, C.-C., Shih, T.-P., Ko, W.-C., Tang, H.-J., Hsueh, P.-R., Severe acute respiratory syndrome coronavirus 2 (sars-cov-2) and coronavirus disease-2019 (covid-19): The epidemic and the challenges (2020) Int J Antimicrob Agents, 55 (3); Gautret, P., Lagier, J.-C., Parola, P., Meddeb, L., Mailhe, M., Doudier, B., Courjon, J., Dupontet, H.T., Hydroxychloroquine and azithromycin as a treatment of covid-19: Results of an open-label non-randomized clinical trial (2020) International Journal of Antimicrobial Agents, 56 (1); Chaos Solitons & Fractals, Page, p. 2020; Shah, F.M., Joy, S.K.S., Ahmed, F., Hossain, T., Humaira, M., Ami, A.S., Paulmd, S., Ahmed, A.R.K.J., A comprehensive survey of covid-19 detection using medical images (2021) SN Computer Science, 2 (6), pp. 1-22; Mehta, N., Shukla, S., Pandemic analytics: How countries are leveraging big data analytics and artificial intelligence to fight covid-19? (2022) SN Computer Science, 3 (1), pp. 1-20; Shinde, G.R., Kalamkar, A.B., Mahalle, P.N., Dey, N., Chaki, J., Hassanien, A.E., Forecasting models for coronavirus disease (Covid-19): A survey of the state-of-the-art (2020) SN Computer Science, 1 (4), pp. 1-15; Chiroma, H., Ezugwu, A.E., Jauro, F., Al-Garadi, M.A., Abdullahi, I.N., Shuib, L., Early survey with bibliometric analysis on machine learning approaches in controlling covid-19 outbreaks (2020) PeerJ Computer Science, 6; Julian Luengo, S.G., José Antonio Sáez, Victoria Lopez, and Francisco Herrera. A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning (2012) IEEE Transactions on Knowledge and Data Engineering, 25 (4), pp. 734-750; Muhammad, I., Yan, Z., Supervised machine learning approaches: A survey (2015) ICTACT Journal on Soft Computing, 5 (3); Sotiris, B., Kotsiantispintelas, I.Z., (2007); Sen, P.C., Hajra, M., Ghosh, M., Supervised classification algorithms in machine learning: A survey and review (2020) Emerging Technology in Modelling and Graphics, pp. 99-111. , Springer; Bangdiwala, S.I., Regression: simple linear (2018) International journal of injury control and safety promotion, 25 (1), pp. 113-115; Connelly, L., Logistic regression (2020) Medsurg Nurs, 29 (5), pp. 353-354; Jinbo Xiong, H.W., Mingwei Lin, Z.Y., Ren, J., Research survey on support vector machine (2017) Proceedings of the 10Th EAI International Conference on Mobile Multimedia Communications, pp. 95-103; Rahmanmd, M.M., Md, I., Hossen, M., Al-Rakhamiet, M.S., Machine learning approaches for tackling novel coronavirus (Covid-19) pandemic (2021) Sn Computer Science, 2 (5), pp. 1-10; Mr Brijain, R.P., Kushikrana, K., (2014); Breiman, L., (2001) Random forests. Machine learning, 45 (1), pp. 5-32; Robert Tibshirani, T.H., Friedman, J., Random forests (2009) The Elements of Statistical Learning, pp. 587-604. , Springer; Moubayed, A., Injadat, M., Nassif, A.B., Lutfiyya, H., Shami, A., E-learning: Challenges and research opportunities using machine learning data analytics (2018) IEEE Access, 6, pp. 39117-39138; Graupe, D., (2019) Principles of Artificial Neural Networks: Basic Designs to Deep Learning, , World Scientific, March; Duval, F., (2018) Artificial Neural Networks: Concepts, , CreateSpace Independent Publishing Platform, Tools and Techniques Explained for Absolute Beginners. Data Sciences; Jentzen, A., Von Wurstemberger, P., Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates (2020) J Complex, 57; Gupta, M.K., Chandra, P., (2019); Chao, G., Luo, Y., Ding, W., Recent advances in supervised dimension reduction: A survey (2019) Machine learning and knowledge extraction, 1 (1), pp. 341-358; Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y., Deep learning, volume 1 (2016) MIT Press Cambridge; Amanullah Asraf, M., Haque, I.M., Deep learning applications to combat novel coronavirus (covid-19) pandemic (2020) SN Computer Science, 1 (6), pp. 1-7; Albawi, S., Mohammed, T.A., Al-Zawi, S., Understanding of a convolutional neural network (2017) 2017 International Conference on Engineering and Technology (ICET), pp. 1-6. , Ieee; Medsker, L.R., Jain, L.C., (2001); Schuster, M., Paliwal, K.K., Bidirectional recurrent neural networks (1997) IEEE transactions on Signal Processing, 45 (11), pp. 2673-2681; Tom White, A.C., Kai Arulkumaran, V.D., Sengupta, B., Bharath, A.A., Generative adversarial networks: An overview (2018) IEEE Signal Processing Magazine, 35 (1), pp. 53-65; Wiering, M.A., Martijn Van Otterlo (2012) Reinforcement Learning. Adaptation, Learning, and Optimization, 12 (3), p. 729; Jang, B., Kim, M., Harerimana, G., Kim, J.W., Q-learning algorithms: A comprehensive classification and applications (2019) IEEE Access, 7, pp. 133653-133667; Zhang Nan, Y.H., Duan Meiyu, Z.R., Pan Jiahui, X.T., Huang Juanjuan, P.E., Xiaoming Xu, Z.Y., Severity detection for the coronavirus disease, et al. (covid-19) patients using a machine learning model based on the blood and urine tests (2019) Frontiers in Cell and Developmental Biology, 683, p. 2020; Hassanien, A.E., Mahdy, L.N., Ezzat, K.A., Elmousalami, H.H., Ella, H.A., (2020) Automatic X-Ray Covid-19 Lung Image Classification System Based on Multi-Level Thresholding and Support Vector Machine. Medrxiv; Detection of coronavirus disease (covid-19) based on deep features (2020) Preprints, p. 2020. , 2020030300; Gang Liu, L.S., Nannan Shi, F.S., Shenyang Li, F.L., Weihan Zhang, P.L., Yongbin Zhang, X.J., Combination of four clinical indicators predicts the severe/critical symptom of patients infected covid-19 (2020) Journal of Clinical Virology; Singh, M., Bansal, S., Ahuja, S., Dubey, R.K., Panigrahi, B.K., Dey, N., Transfer learning based ensemble support vector machine model for automated covid-19 detection using lung computerized tomography scan data (2021) Medical & biological engineering & computing, 59 (4), pp. 825-839; (2020) Chaos, Solitons & Fractals, p. 139; Nour, M., Cömert, Z., Polat, K., A novel medical diagnosis model for covid-19 infection detection based on deep features and bayesian optimization (2020) Applied Soft Computing; Tabrizchi, H., Mosavi, A., Szabo-Gali, A., Felde, I., Nadai, L., Rapid covid-19 diagnosis using deep learning of the computerized tomography scans (2020) 2020 IEEE 3Rd International Conference and Workshop in Óbuda on Electrical and Power Engineering, pp. 000173-000178. , IEEE; Qian Yu, H.Y., Yifei Huang, C.L., Chuxiao Shao, Z.J., Baoyi Ma, H.Z., Guanghang Xie, Y.W., Machine learning-based ct radiomics method for predicting hospital stay in patients with pneumonia associated with sars-cov-2 infection: A multicenter study Annals of Translational Medicine, 8 (14), p. 2020; Xueqing Peng, W.S., Zenghui Cheng, T.L., Shuyi Yang, H.L., Feng Li, J.Z., Xinlei Zhang, M.W., A deep learning-based quantitative computed tomography model in predicting the severity of covid-19: A retrospective study in 196 patients Annals of Translational Medicine, 9 (3), p. 2021; An interpretable mortality prediction model for covid-19 patients (2020) Nature Machine Intelligence, pp. 1-6; Aya Salamaashraf Darwsihaboul Ella Hassanien, Artificial intelligence approach to predict the covid-19 patient’s recovery (2021) In Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, pp. 121-133. , Springer; Rajan Guptagaurav Pandeypoonam Chaudharysaibal Kumar Pal, (2020) Seir and regression model based covid-19 outbreak predictions in india., , medRxiv; Xingdong Chenzhenqiu Liu, (2020) Early prediction of mortality risk among severe covid-19 patients using machine learning., , medRxiv; Analysis on novel coronavirus (Covid-19) using machine learning methods (2020) Chaos, Solitons & Fractals; Francesco Paparo, J.M., Lorenzo Bacigalupo, I.M., Silvia Perugin, A.V., Ennio Biscaldi, B., Giancarlo Antonucci, E.M., Cremonesi, P., Evaluation of novel coronavirus disease (Covid-19) using quantitative lung ct and clinical data: Prediction of short-term outcome (2020) European Radiology Experimental, 4 (1), pp. 1-10; Khanday, A.M.U.D., Rabani, S.T., Khan, Q.R., Rouf, N., Din, M.M.U., Machine learning based approaches for detecting covid-19 using clinical text data (2020) International Journal of Information Technology, 12 (3), pp. 731-739; He, S., Yang, Yu Hou, Ljiljana V Vasovic, Peter AD Steel, Amy Chadburn, Sabrina (2020) E Racine-Brzostek, Priya Velu, Melissa M Cushing, Massimo Loda, Rainu Kaushal, Et Al. Routine Laboratory Blood Tests Predict Sars-Cov-2 Infection Using Machine Learning. Clinical Chemistry, 66 (11), pp. 1396-1404; Saqib, M., Forecasting covid-19 outbreak progression using hybrid polynomial-bayesian ridge regression model (2021) Appl Intell, 51 (5), pp. 2703-2713; Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification (2020), arXiv preprint arXiv:2003.09860; Ali Kashif, C.I., Atharva Peshkar, B., Sujatha, R., (2020); Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., Cabitza, F., Detection of covid-19 infection from routine blood exams with machine learning: a feasibility study (2020) J Med Syst, 44 (8), pp. 1-12; Lj Muhammad, E.A.A., Sani Sharif, U., Abdulkadir Ahmadchakraborty, C., ; Deep learning-based decision-tree classifier for covid-19 diagnosis from chest x-ray imaging (2020) Frontiers in Medicine, 7, p. 427; Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Meng, X., A deep learning algorithm using ct images to screen for corona virus disease (covid-19) (2021) Eur Radiol, 31 (8), pp. 6096-6104; Ali Narin Ceren Kayaziynet Pamuk, Automatic detection of coronavirus disease (Covid-19) using x-ray images and deep convolutional neural networks (2021) Pattern Analysis and Applications, pp. 1-14; Halgurd, S., Maghdidarasasaadkayhan Zrar Ghafoorali Safaa Sadiqseyedali Mirjalilimuhammad Khurram Khan, T., Diagnosing covid-19 pneumonia from x-ray and ct images using deep learning and transfer learning algorithms (2021) Multimodal Image Exploitation and Learning 2021, 11734; Wang, B., Jin, S., Yan, Q., Haibo, X., Luo, C., Wei, L., Zhao, W., Zhengqing, X., Ai-assisted ct imaging analysis for covid-19 screening: Building and deploying a medical ai system (2021) Appl Soft Comput, 98; Chen, J., Lianlian, W., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Yang, X., Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography (2020) Sci Rep, 10 (1), pp. 1-11; Apostolopoulos, I.D., Mpesiana, T.A., Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks (2020) Physical and Engineering Sciences in Medicine, p. page 1; Chunhua Shen, and Yong Xia (2020) Covid-19 Screening on Chest X-Ray Images Using Deep Learning Based Anomaly Detection. Arxiv Preprint Arxiv, 2003, p. 12338; Ghoshal, B., Tucker, A., (2020) Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (Covid-19) Detection. Arxiv Preprint Arxiv, 2003, p. 10769; Toraman, S., Alakus, T.B., Capsnet, I.T.C., A novel artificial neural network approach to detect covid-19 disease from x-ray images using capsule networks. Chaos (2020) Solitons & Fractals, p. 140; Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., Scherpereel, A., Deep learning on chest x-ray images to detect and evaluate pneumonia cases at the era of covid-19 (2021) J Med Syst, 45 (7), pp. 1-10; Results of 10 convolutional neural networks Computers in Biology and Medicine, Page, p. 2020; Xiaowei, X., Jiang, X., Ma, C., Peng, D., Li, X., Lv, S., Liang, Y., Junwei, S., A deep learning system to screen novel coronavirus disease 2019 pneumonia (2020) Engineering, 6 (10), pp. 1122-1129; Deep learning model for diagnosis of corona virus disease from ct images (2020) International Journal of Scientific & Engineering Research, 11, pp. 273-278; Harrison X Bai, R., Wang, Z., Xiong, B., Hsieh, K., Chang, K., Halsey, T.M.L., Tran, J.W., Wangshi, L.-B., Ai augmentation of radiologist performance in distinguishing covid-19 from pneumonia of other etiology on chest ct (2020) Radiology; Loey, M., Smarandache, F., Eldeen, N., Khalifa, M., Within the lack of chest covid-19 x-ray dataset: A novel detection model based on gan and deep transfer learning (2020) Symmetry, 12 (4), p. 651; Singh, D., Kumar, V., Kaur, M., Classification of covid-19 patients from chest ct images using multi-objective differential evolution–based convolutional neural networks (2020) European Journal of Clinical Microbiology & Infectious Diseases, pp. 1-11; Subhankar Ghosh, H.M., Sk Obaidullah, A.D., Kaushik Roy, K.C.S., Shallow convolutional neural network for covid-19 outbreak screening using chest x-rays (2021) Cognitive Computation, pp. 1-14; Şaban Öztürk, U.Ö., Barstugan, M., Coronavirus (Covid-19) classification using deep features fusion and ranking technique (2020) Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, pp. 281-295. , Springer; Burhan Ergen, M.T., Cömert, Z., Covid-19 detection using deep learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches (2020) Computers in Biology and Medicine; Pathak, Y., Shukla, P.K., Tiwari, A., Stalin, S., Singh, S., Shukla, P.K., Deep transfer learning based classification model for covid-19 disease (2020) IRBM; Youness Chawki, K.E.A., Idri, A., Automated methods for detection and classification pneumonia based on x-ray images using deep learning (2021) Artificial Intelligence and Blockchain for Future Cybersecurity Applications, pp. 257-284. , Springer; Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Xingwang, W., Zha, Y., Wang, K., Clinically applicable ai system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography (2020) Cell, 181 (6), pp. 1423-1433; Rajaraman, S., Antani, S., Weakly labeled data augmentation for deep learning: A study on covid-19 detection in chest x-rays (2020) Diagnostics, 10 (6), p. 358; Interpretable artificial intelligence framework for covid-19 screening on chest x-rays (2020) Experimental and Therapeutic Medicine, 20 (2), pp. 727-735; Cohen, J.P., . Covid chest x-ray dataset., , https://github.com/ieee8023/covid-chestxray-dataset,2020, Github, [Accessed 20 September 2021]; Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K., Deep transfer learning-based automated detection of covid-19 from lung ct scan slices (2021) Applied Intelligence, 51 (1), pp. 571-585; Yujin Ohsangjoon Parkjong Chul Ye, Deep learning covid-19 features on cxr using limited training data sets (2020) IEEE Transactions on Medical Imaging, 39 (8), pp. 2688-2700; El Asnaoui, K., Chawki, Y., Using x-ray images and deep learning for automated detection of coronavirus disease (2021) J Biomol Struct Dyn, 39 (10), pp. 3615-3626; Muhammad, E.H., Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi, et al. Can ai help in screening viral and covid-19 pneumonia (2020) IEEE Access, p. 8; Apostolopoulos, I.D., Aznaouridis, S.I., Tzani, M.A., Extracting possibly representative covid-19 biomarkers from x-ray images with deep learning approach and image data related to pulmonary diseases (2020) Journal of Medical and Biological Engineering, 40 (3), pp. 462-469; A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception and resnet50v2 (2020) Informatics in Medicine Unlocked, p. 100360; Abbas, A., Abdelsamea, M.M., Gaber, M.M., Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network (2021) Applied Intelligence, 51 (2), pp. 854-864; Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., Mohammadi, A., Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images (2020) Pattern Recognition Letters, 138, pp. 638-643; Mooney, P., (2020) Kaggle Chest X-Ray Images (Pneumonia) Dataset, , https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia; Brunese, L., Mercaldo, F., Reginelli, A., Santone, A., Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays (2020) Comput Methods Programs Biomed, 196; Jin, C., Chen, W., Cao, Y., Zhanwei, X., Tan, Z., Zhang, X., Deng, L., Shi, H., Development and evaluation of an artificial intelligence system for covid-19 diagnosis (2020) Nat Commun, 11 (1), pp. 1-14; Dipayan Das Kc Santoshumapada Pal, Truncated inception net: Covid-19 outbreak screening using chest x-rays (2020) Physical and Engineering Sciences in Medicine, 43 (3), pp. 915-925; Sohaib Asifyi Wenhuihou Jinsi Jinhai., Classification of covid-19 from chest x-ray images using deep convolutional neural network (2020) 2020 IEEE 6Th International Conference on Computer and Communications (ICCC), pp. 426-433; Automated diagnosis of covid-19 with limited posteroanterior chest x-ray images using fine-tuned deep neural networks (2021) Appl Intell, 51 (5), pp. 2689-2702; Stein, A., Pneumonia dataset annotation methods. Rsna pneumonia detection challenge discussion., , https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/64723,2018; Sema Candemirsameer Antani, S.J., Pu-Xuan Lu, Y.-X.J.W., Thoma, G., Two public chest x-ray datasets for computer-aided screening of pulmonary diseases (2014) Quantitative Imaging in Medicine and Surgery, 4 (6), p. 475; Shelke, A., Inamdar, M., Shah, V., Tiwari, A., Hussain, A., Chafekar, T., Mehendale, N., Chest x-ray classification using deep learning for automated covid-19 screening (2021) SN computer science, 2 (4), pp. 1-9; Rajaraman, S., Siegelman, J., Alderson, P.O., Folio, L.S., Folio, L.R., Antani, S.K., Iteratively pruned deep learning ensembles for covid-19 detection in chest x-rays (2020) Ieee Access, 8, pp. 115041-115050; Luz, E., Silva, P., Silva, R., Silva, L., Guimarães, J., Miozzo, G., Moreira, G., Menotti, D., Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images (2022) Research on Biomedical Engineering, 38 (1), pp. 149-162; Neha Gianchandani, A.J., Vijay Kumar, D.S., Kaur, M., Classification of the covid-19 infected patients using densenet201 based deep transfer learning (2020) Journal of Biomolecular Structure and Dynamics, pp. 1-8; Sharma, S., Drawing insights from covid-19-infected patients using ct scan images and machine learning techniques: a study on 200 patients (2020) Environ Sci Pollut Res, 27 (29), pp. 37155-37163; Jiantao, P., Leader, A., Bandos, J., Shipang, D., Juezhao, Y., Yang, S., Ke, Y., Fieldet, A., Any unique image biomarkers associated with covid-19? (2020) European Radiology, 30 (11), pp. 6221-6227; Alotaibi, A.N., Transfer learning for detecting covid-19 cases using chest x-ray images (2020) International Journal of Machine Learning and Networked Collaborative Engineering, 4 (1), pp. 21-29; Goyal, L., Arora, N., Deep transfer learning approach for detection of covid-19 from chest x-ray images (2020) International Journal of Computer Applications, 975, p. 8887; Narayan Dasnaresh Kumar Manjit Kaurvijay Kumardilbag Singh, N., (2020) Automated deep transfer learning-based approach for detection of covid-19 infection in chest x-rays., , Irbm; Identification of covid-19 samples from chest x-ray images using deep learning: A comparison of transfer learning approaches (2020) Journal of X-Ray Science and Technology, 28 (5), pp. 821-839; Altan, A., Karasu, S., Recognition of covid-19 disease from x-ray images by hybrid model consisting of 2d curvelet transform, chaotic salp swarm algorithm and deep learning technique (2020) Chaos, Solitons & Fractals, 140; Qianqian Ni Zhi Yuan Sun Li Qiwen Chen Yi Yangli Wang Xinyuan Zhangliu Yang Yi Fangzijian Xing, A deep learning approach to characterize 2019 coronavirus disease (Covid-19) pneumonia in chest ct images (2020) European Radiology, 30 (12), pp. 6517-6527; Phuong Nguyenludovico Iovinomichele Flamminilinh Tuan Linh, (2020) Deep learning for automated recognition of covid-19 from chest x-ray images., , medRxiv; Md Milon Islammd Zabirul Islamamanullah Asrafweiping Ding., (2020) Diagnosis of covid-19 from x-rays using combined cnn-rnn architecture with transfer learning. MedRxiv; Artificial intelligence–enabled rapid diagnosis of patients with covid-19 (2020) Nature Medicine, 26 (8), pp. 1224-1228; A deep-learning-based framework for automated diagnosis of covid-19 using x-ray images (2020) Information, 11 (9), p. 419; Perumal, V., Narayanan, V., Rajasekar, S.J.S., Detection of covid-19 using cxr and ct images using transfer learning and haralick features (2021) Appl Intell, 51 (1), pp. 341-358; Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging (2021) IEEE Sensors Journal, 21 (14), pp. 16301-16314; Zebin, T., Rezvy, S., Covid-19 detection and disease progression visualization: Deep learning on chest x-rays for classification and coarse localization (2021) Appl Intell, 51 (2), pp. 1010-1021; Abraham, B., Nair, M.S., Computer-aided detection of covid-19 from x-ray images using multi-cnn and bayesnet classifier (2020) Biocybernetics and biomedical engineering, 40 (4), pp. 1436-1445; Arasismaelabdulkadir Şengür, M., Deep learning approaches for covid-19 detection based on chest x-ray images (2020) Expert Systems with Applications, 164; Tripti Goelmuruganseyedali Mirjalili, R., Optconet: An optimized convolutional neural network for an automatic diagnosis of covid-19 (2021) Applied Intelligence, 51 (3), pp. 1351-1366; Vedant Bahelsofia Pillai, Detection of covid-19 using chest radiographs with intelligent deployment architecture (2020) In Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, pp. 117-130. , Springer; Attention-based vgg-16 model for covid-19 chest x-ray image classification (2021) Appl Intell, 51 (5), pp. 2850-2863; Rachna Jain Meenu Guptasoham Tanejajude Hemanth, D., Deep learning based detection and analysis of covid-19 on chest x-ray images (2021) Applied Intelligence, 51 (3), pp. 1690-1700; Yaşar, H., Ceylan, M., A new deep learning pipeline to detect covid-19 on chest x-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks (2021) Appl Intell, 51 (5), pp. 2740-2763; Nour Eldeen M Khalifamohamed Hamed N Tahaaboul Ella Hassaniensarah Hamedtaha, N., The detection of covid-19 in ct medical images: A deep learning approach (2020) In Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, pp. 73-90. , Springer; Subhankar Ghosh, H.M., Sk Md, A.D., Kc Santosh, O., Roy, K., Deep neural network to detect covid-19: One architecture for both ct scans and chest x-rays (2021) Applied Intelligence, 51 (5), pp. 2777-2789; Hasan, N., Bao, Y., Shawon, A., Huang, Y., Densenet convolutional neural networks application for predicting covid-19 using ct image (2021) SN computer science, 2 (5), pp. 1-11; Joerg Vandenhirtz, A.S., David Bacsa, J.N., Riley, M., Identification of images of covid-19 from chest x-rays using deep learning: Comparing cognex visionpro deep learning 1. 0 TM software with open source convolutional neural networks (2021) SN Computer Science, 2 (3), pp. 1-16; Pierre, G.B., Moutounet-Cartan (2020) Deep Convolutional Neural Networks to Diagnose Covid-19 and Other Pneumonia Diseases from Posteroanterior Chest X-Rays. Arxiv Preprint Arxiv, 2005, p. 00845; A deep learning framework for coronavirus disease (Covid-19) detection in x-ray images. 2020. [, , https://www.researchsquare.com/article/rs-26500/v1, online] Available; Salih, S.Q., Abdulla, H.K., Ahmed, Z.S., Surameery, N.M.S., Rashid, R.D., Modified alexnet convolution neural network for covid-19 detection using chest x-ray images (2020) Kurdistan Journal of Applied Research, pp. 119-130. , pages; Cvdeep-covid-19 detection model (2021) SN Computer Science, 2 (3), pp. 1-16; Computer vision and radiology for covid-19 detection (2020) In 2020 International Conference for Emerging Technology (INCET), pp. 1-5. , IEEE; Diagnosis of covid-19 using ct scan images and deep learning techniques (2021) Emergency Radiology, 28 (3), pp. 497-505; Sharma, V., Dyreson, C., Covid-19 screening using residual attention network an artificial intelligence approach (2020) 2020 19Th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1354-1361. , IEEE; Deep learning system for covid-19 diagnosis aid using x-ray pulmonary images Applied Sciences, 10 (13), p. 4640, 2020; Sekeroglu, B., Ozsahin, I., Detection of covid-19 from chest x-ray images using convolutional neural networks (2020) SLAS TECHNOLOGY: Translating Life Sciences Innovation; Maguolo, G., Nanni, L., A critic evaluation of methods for covid-19 automatic detection from x-ray images (2021) Information Fusion, 76, pp. 1-7; Hossein Abbasimehr and Reza Paki. Prediction of covid-19 confirmed cases combining deep learning methods and bayesian optimization . Chaos, Solitons & Fractals, Page, p. 2020; Hira, S., Bai, A., Hira, S., An automatic approach based on cnn architecture to detect covid-19 disease from chest x-ray images (2021) Appl Intell, 51 (5), pp. 2864-2889; Alshazly, H., Linse, C., Barth, E., Martinetz, T., Explainable covid-19 detection using chest ct scans and deep learning (2021) Sensors, 21 (2), p. 455; Automatic evaluation of the lung condition of covid-19 patients using x-ray images and convolutional neural networks (2021) Journal of Personalized Medicine, 11 (1), p. 28; Afifi, A., Hafsa, N.E., Ali, M.A.S., Alhumam, A., Alsalman, S., An ensemble of global and local-attention based convolutional neural networks for covid-19 diagnosis on chest x-ray images (2021) Symmetry, 13 (1), p. 113; Shervin Minaeerahele Kafiehmilan Sonkashakib Yazdanighazaleh Jamalipour Soufi, Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning (2020) Medical Image Analysis, 65; Jelodar, H., Wang, Y., Orji, R., Huang, S., Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach (2020) IEEE J Biomed Health Inform, 24 (10), pp. 2733-2742; Jiang, Z., Menghan, H., Gao, Z., Fan, L., Dai, R., Pan, Y., Tang, W., Yong, L., Detection of respiratory infections using rgb-infrared sensors on portable device (2020) IEEE Sens J, 20 (22), pp. 13674-13681; Mohammed, A., Wang, C., Zhao, M., Ullah, M., Naseem, R., Wang, H., Pedersen, M., Cheikh, F.A., Weakly-supervised network for detection of covid-19 in chest ct scans (2020) Ieee Access, 8, pp. 155987-156000; Md, Z.I., Mdasraf, M.I., A combined deep cnn-lstm network for the detection of novel coronavirus (Covid-19) using x-ray images (2020) Informatics in Medicine Unlocked, 20; Aslan, M.F., Unlersen, M.F., Sabanci, K., Durdu, A., Cnn-based transfer learning–bilstm network: A novel approach for covid-19 infection detection (2021) Applied Soft Computing, 98; Amine Rguibi, M., Moussa, N., Madani, A., Abdessadak Aaroud, and Khalid Zine-Dine. Forecasting covid-19 transmission with arima and lstm techniques in morocco (2022) SN Computer Science, 3 (2), pp. 1-14; László Róbert Kolozsvári, Tamás Bérczes, András Hajdu, Rudolf Gesztelyi, Attila Tiba, Imre Varga, B Ala’a, Gergő József Szőllősi, Szilvia Harsányi, Szabolcs Garbóczy, et al. Predicting the epidemic curve of the coronavirus (sars-cov-2) disease (covid-19) using artificial intelligence: An application on the first and second waves. Informatics in Medicine Unlocked, 25:100691, 2021.; Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Chong, Y., Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images (2021) IEEE/ACM Trans Comput Biol Bioinf, 18 (6), pp. 2775-2780; Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct (2020) Radiology; (2020) Deep learning-based detection for covid-19 from chest ct using weak label, , . medRxiv; Ucar, F., Korkmaz, D., Covidiagnosis-net: Deep bayes-squeezenet based diagnosis of the coronavirus disease 2019 (covid-19) from x-ray images (2020) Med Hypotheses, 140; Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U Rajendra Acharya. Automated detection of covid-19 cases using deep neural networks with x-ray images (2020) Computers in Biology and Medicine, 121; Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases (2017) In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097-2106; Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images (2020) Scientific Reports, 10 (1), pp. 1-12; Jérémie Roulin, and Nina Wiedemann (2020) Pocovid-Net: Automatic Detection of Covid-19 from a New Lung Ultrasound Imaging Dataset (Pocus)., , arXiv preprint arXiv:2004.12084; Shuo Wang Yunfei Zhaweimin Li Qingxia Wuxiaohu Li Meng Niumeiyun Wang Xiaoming Qiuhongjun Li He Yu, A fully automatic deep learning system for covid-19 diagnostic and prognostic analysis European Respiratory Journal, 56 (2), p. 2020; (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; Tanvir Mahmudmd Awsafur Rahmanshaikh Anowarul Fattah, 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 (2020) Computers in Biology and Medicine, 122; Siddhartha, M., Covidlite, A.S., (2020) A Depth-Wise Separable Deep Neural Network with White Balance and Clahe for Detection of Covid-19. Arxiv Preprint Arxiv, 2006, p. 13873; Ahmed, S., Yap, M.H., Tanmd, M.H., (2020) Reconet: Multi-Level Preprocessing of Chest X-Rays for Covid-19 Detection Using Convolutional Neural Networks. Medrxiv; Irvin, J., Rajpurkar, P., Ko, M., Yifan, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Shpanskaya, K., Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison (2019) In Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 590-597; Mahdiyar Molahasani, A.H., Younhee Choi, M., Deivalakshmi, S., Ko, S., ; Turkoglu, M., Covidetectionet: Covid-19 diagnosis system based on x-ray images using features selected from pre-learned deep features ensemble (2021) Appl Intell, 51 (3), pp. 1213-1226; Detection of novel covid-19 in chest x-ray images by leveraging deep transfer learning models (2022) ICDSMLA 2020, Pages 431–447. Springer; Al-Bawi, A., Al-Kaabi, K., Jeryo, M., Al-Fatlawi, A., Ccblock: An effective use of deep learning for automatic diagnosis of covid-19 using x-ray images (2020) Research on Biomedical Engineering, pp. 1-10; Covidxception-net: A bayesian optimization-based deep learning approach to diagnose covid-19 from x-ray images (2022) SN Computer Science, 3 (2), pp. 1-22; Covidctnet: An open-source deep learning approach to diagnose covid-19 using small cohort of ct images (2021) NPJ Digital Medicine, 4 (1), pp. 1-10; Et al. Ai-corona (2021) Plos One, 16 (5); Saeedizadeh, N., Minaee, S., Kafieh, R., Yazdani, S., Sonka, M., Covid tv-unet: Segmenting covid-19 chest ct images using connectivity imposed unet (2021) Computer Methods and Programs in Biomedicine Update, 1; Kumar Jaiswal, A., Tiwari, P., Kumar Rathi, V., Qian, J., Pandey, H.M., Hugo C Albuquerque, V., (2020) Covidpen: A Novel Covid-19 Detection Model Using Chest X-Rays and Ct Scans. Medrxiv; Chowdhurymd, N.K., Kabir, M.R.A., (2020) Pdcovidnet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting Covid-19 from Chest X-Ray Images. Health Information Science and Systems, 8, pp. 1-14; Xiang, Y., Wang, S.-H., Zhang, Y.-D., Cgnet: A graph-knowledge embedded convolutional neural network for detection of pneumonia (2020) Information Processing & Management, 58 (1); Covidgr dataset and covid-sdnet methodology for predicting covid-19 based on chest x-ray images (2020) IEEE Journal of Biomedical and Health Informatics, 24 (12), pp. 3595-3605; Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Dehghani, M., Artificial intelligence and covid-19: deep learning approaches for diagnosis and treatment (2020) IEEE Access, 8, pp. 109581-109595; Ahmed A Abd El-Latif, et al. Deploying machine and deep learning models for efficient data-augmented detection of covid-19 infections (2020) Viruses, 12 (7), p. 769; Om Elzeki, M.Y.S., ; Abdullah Farid, A., Selim, G.I., Awad, H., Khater, A., A novel approach of ct images feature analysis and prediction to screen for corona virus disease (Covid-19) (2020) International Journal of Scientific and Engineering Research, 11 (3), pp. 1-9; Hwang, E.J., Kim, H., Yoon, S.H., Goo, J.M., Park, C.M., Implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for covid-19 (2020) Korean journal of radiology, 21 (10), p. 1150; Amyar, A., Modzelewski, R., Li, H., Ruan, S., Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: Classification and segmentation (2020) Comput Biol Med, 126; Heewon Chung, H.K., Wu Seong Kang, Kyung Won Kim, Youngbin Shin, Seung Ji Kang, Jae Hoon Lee, Young Jun Kim, Nan Yeol Kim, Hyunseok Jung, et al. Covid-19 pneumonia diagnosis using a simple 2d deep learning framework with a single chest ct image: Model development and validation (2020) Journal of Medical Internet Research, 22 (6); Sushmita Mitra, S.B., Saha, N., Deep learning for screening covid-19 using chest x-ray images (2020) 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2521-2527. , IEEE; Elghamrawy, S., An h 2 o’s deep learning-inspired model based on big data analytics for coronavirus disease (Covid-19) diagnosis (2020) Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, pp. 263-279. , Springer; Daniel Kermany Kang Zhangmichael Goldbaum, Large dataset of labeled optical coherence tomography (Oct) and chest x-ray images Mendeley Data, , https://doi.org/10.17632/rscbjbr9sj,3,2018; Sheeba Rani, A.S., Gupta, D., Artificial intelligence-based classification of chest x-ray images into covid-19 and other infectious diseases (2020) International Journal of Biomedical Imaging, 2020; Hammam, A.A., Elmousalami, H.H., Hassanien, A.E., Stacking deep learning for early covid-19 vision diagnosis (2020) Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, pp. 297-307. , Springer; Ak Abdul, S.N.M., Covid-19 diagnostics from the chest x-ray image using corner-based weber local descriptor (2020) In Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, pp. 131-145; Li, D., Zhangjie, F., Jun, X., Stacked-autoencoder-based model for covid-19 diagnosis on ct images (2021) Appl Intell, 51 (5), pp. 2805-2817; Al-Antari, M.A., Hua, C.-H., Bang, J., Lee, S., Fast deep learning computer-aided diagnosis of covid-19 based on digital chest x-ray images (2021) Applied Intelligence, 51 (5), pp. 2890-2907; Le, L., Xiaosong Wang, Gustavo Carneiro, and Lin Yang. Deep learning and convolutional neural networks for medical imaging and clinical informatics. Cham (2019) Switzerland:Springer Switzerland, p. 6330; Aayush Jaiswal, N.G., Vijay Kumar, D.S., Kaur, M., Rapid covid-19 diagnosis using ensemble deep transfer learning models from chest radiographic images (2020) Journal of Ambient Intelligence and Humanized Computing, pp. 1-13; Asraf Amanullah, . Covid19 penumonia normal chest xray pa dataset., , https://www.kaggle.com/amanullahasraf/covid19-pneumonia-normal-chest-xray-pa-dataset,2021, [Accessed 12 November 2021]; Hasan, A.M., AL-Jawad, M.M., Jalab, H.A., Shaiba, H., Ibrahim, R.W., AL-Shamasneh, A., Classification of covid-19 coronavirus, pneumonia and healthy lungs in ct scans using q-deformed entropy and deep learning features (2020) Entropy, 22 (5), p. 517; Ali Imran Iryna Posokhovahaneya N Qureshiusama Masood, Muhammad Sajid Riaz, Kamran Ali, Charles N John, MD Iftikhar Hussain, and Muhammad Nabeel. Ai4covid-19: Ai enabled preliminary diagnosis for covid-19 from cough samples via an app (2020) Informatics in Medicine Unlocked, 20; Sahlol, T., New machine learning method for image-based diagnosis of covid-19 (2020) Plos One, 15 (6); Karthik, R., Menaka, R., Hariharan, M., Learning distinctive filters for covid-19 detection from chest x-ray using shuffled residual cnn (2020) Applied Soft Computing, p. page; Soarov Chakraborty Shourav Paul, K.M., (2022) . A transfer learning-based approach with deep cnn for covid-19-and pneumonia-affected chest x-ray image classification. SN Computer Science, 3 (1), pp. 1-10; Tanujit Chakraborty and Indrajit Ghosh. Real-time forecasts and risk assessment of novel coronavirus (covid-19) cases (2020) Solitons & Fractals, Page; Turker Tuncer Sengul Doganfatih Ozyurt, (2020) An automated residual exemplar local binary pattern and iterative relieff based corona detection method using lung x-ray image., p. page. , Chemometrics and Intelligent Laboratory Systems; Shreshth Tulishikhar Tulirakesh Tulisukhpal Singh Gill, (2020) Predicting the growth and trend of covid-19 pandemic using machine learning and cloud computing., p. page. , Internet of Things; Rodolfo, M., Pereiradiego Bertolinilucasteixeira, O., Carlos N Silla Jr, and Yandre MG Costa. Covid-19 identification in chest x-ray images on flat and hierarchical classification scenarios (2020) Computer Methods and Programs in Biomedicine, 194; Os Albahri, J.R.A.-O., Aa Zaidan, A.S.A., Bb Zaidan, M.M., Salih, A., Qaysrt Mohammed, K.A.D., Karrar Hameed Abdulkareem, et al. Helping doctors hasten covid-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel mcdm methods (2020) Computer Methods and Programs in Biomedicine, 196; Wang, P., Zheng, X., Li, J., Zhu, B., Prediction of epidemic trends in covid-19 with logistic model and machine learning technics (2020) Chaos, Solitons & Fractals, 139; Covidiag: A clinical cad system to diagnose covid-19 pneumonia based on ct findings (2021) European Radiology, 31 (1), pp. 121-130; Singh, K.K., Kumar, S., Dixit, P., Bajpai, M.K., Kalman filter based short term prediction model for covid-19 spread (2021) Applied Intelligence, 51 (5), pp. 2714-2726; Brunese, L., Martinelli, F., Mercaldo, F., Santone, A., Machine learning for coronavirus covid-19 detection from chest x-rays (2020) Procedia Computer Science, 176, pp. 2212-2221
PY - 2022
Y1 - 2022
N2 - The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,..) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
AB - The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,..) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
KW - Artificial intelligence
KW - CNN
KW - COVID-19 detection
KW - COVID-19 diagnosis
KW - COVID-19 prediction
KW - Deep learning
KW - Machine learning
U2 - 10.1007/s42979-022-01184-z
DO - 10.1007/s42979-022-01184-z
M3 - Article
VL - 3
JO - SN Computer Science
JF - SN Computer Science
SN - 2662-995X
IS - 4
ER -