COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review
Abstract
:1. Introduction
- ➢
- Determining the significance of available datasets from literature used for the prediction of COVID-19.
- ➢
- To analyze the ML and DL techniques that were applied to detect the COVID-19.
- ➢
- Identification of challenges and future research directions related to the implications of ML/DL techniques for COVID detection.
2. Review Methodology
2.1. Selection of Digital Archives
2.2. Search Code Strategy
Machine and Deep Learning Techniques
2.3. Eligibility Criteria and Article Screening
2.4. Data Segregation and Categorization
3. Results and Discussion
3.1. Analysis of Extracted Data
3.2. Investigation on Classification Performance
4. State-of-the-Art COVID-19 Detection Using Anal Swab-Based Diagnosis
- Stop traveling from in and out of China to control the transmission.
- Developed quarantine centers for suspected cases to get the best treatment.
- China developed mobile apps for tracking suspected, confirmed cases, and interaction with individuals having COVID-19 symptoms.
- Develop public awareness regarding self-protection, epidemiologic investigation, cleaning, and disinfecting the environment.
- The government installed many intelligent based systems to monitor the public temperature, such as airports, metro stations, hospitals, communities.
5. Challenges and Future Research Directions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
ML | Machine Learning |
MLT | Machine Learning Techniques |
CT | Computer Tomography |
CAD | Computer-Aided Diagnosis |
CNN | Convolutional Neural Network |
D.N.N | Deep Neural Network |
DL | Deep Learning |
DT | Decision Tree |
RF | Random Forest |
SVM | Support Vector Machine |
SARS | Severe Acute Respiratory Syndrome |
K-NN | K-Nearest Neighbors |
L.S.T.M. | Long Short-Term Memory |
SD | Science Direct |
W.O.S | Web of Science |
X-ray | X-radiation |
M.R.I | Magnetic Resource Imaging |
MERS | Middle East Respiratory syndrome |
RT-PCR | Reverse Transcription Polymerase Chain Reaction |
A.U.C. | Area under the Receiver Operating Characteristic Curve |
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Reference | Test Data | COVID-19 Positive | Pneumonia/Other Infection | Healthy | Total | Sources/Links |
---|---|---|---|---|---|---|
[35] | Chest X-ray CT Images | 50 | * NA | 50 | 100 | https://github.com/ieee8023/covid-chestxray-dataset https://www.kaggle.com/paultimothymooney/chest-xray |
[36] | Chest X-ray | 284 | 657 | 310 | 1251 | https://github.com/ieee8023/covid-chestxray-dataset https://www.kaggle.com/paultimothymooney/chest-xray |
[37] | Chest X-ray | 250 | 2753 | 3520 | 6523 | https://github.com/ieee8023/covid-chestxray-dataset https://github.com/muhammedtalo/COVID-19 https://www.kaggle.com/nih-chest-xrays/sample |
[38] | Chest X-ray | 274 | 2051 | 1341 | 3666 | https://www.kaggle.com/tawsifurrahman/covid19-radiography-database https://github.com/agchung/Figure1-COVID-chestxray-dataset https://github.com/ieee8023/covid-chestxray-dataset |
[26] | CT Images | 1296 | 1735 | 1325 | 4356 | https://github.com/bkong999/COVNet |
[39] | CT Images | 510 | 510 | * NA | 1020 | Alexion, Toshiba Medical System, Japan |
[11] | Chest X-ray | 250 | 500 | 1000 | 1750 | https://github.com/ieee8023/COVID-chestxray-dataset |
[40] | CT Images | 219 | 224 | 175 | 618 | Hospital of Zhejiang University Hospital of Wenzhou Hospital of Wenling |
[41] | CT Images | 313 | * NA | 229 | 542 | Union Hospital Tongji Medical College, Huazhong University of Science and Technology, China. |
[42] | CT Images | 325 | 740 | * NA | 1065 | Xi’an Jiaotong University First Affiliated Hospital Nanchang University First Hospital Xi’an No.8 Hospital of Xi’an Medical College, China |
[43] | CT Images | 777 | ** NS | 708 | 1485 | The Third Affiliated Hospital and Sun Yat-Sen Memorial Hospital, Sun Yat-sen University Guangzhou Renmin Hospital of Wuhan University, China |
[44] | CT Images | 306 | * NA | 306 | 612 | University of Medical Science (I.U.M.S.), Iran |
[3] | Chest X-ray | 224 | 700 | 504 | 1428 | https://github.com/ieee8023/covid-chestxray-dataset https://www.kaggle.com/andrewmvd/convid19-X-rays |
[45] | Chest X-ray | 53 | 5526 | 8066 | 13,645 | https://github.com/agchung/Figure1-COVID-chestxray-dataset https://github.com/agchung/Actualmed-COVID-chestxray-dataset https://www.kaggle.com/tawsifurrahman/covid19-radiography-database https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data |
[46] | Chest X-ray | 127 | 127 | 127 | 381 | https://openi.nlm.nih.gov/ www.kaggle.com www.github.com |
[47] | Chest X-ray | 25 | * NA | 25 | 50 | https://github.com/ieee8023/covid-chestxray-dataset https://www.pyimagesearch.com/category/medical/ |
[48] | Chest X-ray | 239 + 2265 (BIMCV) | 4273 + 951 | 1583 | 9311 | Japanese Radiological Scientific Technology (J.R.S.T.) Shenzhen Dataset, Montgomery Dataset University of Montreal, Valencian Region Medical Image Bank (B.I.M.C.V.) https://github.com/ieee8023/covid-chestxray-dataset |
[49] | CT Images | 56 | 52 | 49 | 157 | http://www.chainz.cn/Hospital in Wenzhou, ChinaEI-Camino Hospital, USA |
[50] | CT Images (abdominal) | 53 | ** NS | * NA | 150 | https://www.sirm.org/en/ |
[51] | Chest X-ray | 68 | 2786 | 1583+ 1504 | 5941 | https://github.com/ieee8023/covid-chestxray-dataset |
[52] | CT Images | 133 | ** N.S. | * NA | 199 | Wuhan Pulmonary Hospital, china |
[53] | Chest X-ray andCT Images | 2228 | 3308 | 2381 | 7917 | https://github.com/ChenWWWeixiang/diagnosis_covid19 https://tianchi.aliyun.com/competition/entrance/231601/information Wuhan Union Hospital and Jianghan Mobile cabin Hospital, China |
[54] | CT Images | 877 | * NA | 541 | 1418 | Beijing Tsinghua Changgung Hospital, China, Wuhan No.7 Hospital, Wuhan Leishenshan Hospital, Zhongnan Hospital of Wuhan University, Wuhan, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China |
[55] | Chest X-ray | 423 | 1485 | 1579 | 3487 | https://www.kaggle.com/tawsifurrahman/covid19-radiography-database https://www.sirm.org/category/senza-categoria/covid-19/ https://github.com/ieee8023/covid-chestxray-dataset https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia |
[56] | Chest X-ray and CT Images | 288 | ** NS | 238 | 526 | https://www.bsti.org.uk/training-and-education/covid-19-bsti-imaging-database/ https://radiopaedia.org/articles/normal-chest-imaging-examples?lang=gb https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/metadata |
[57] | Chest X-ray | 100 | ** NS | 1431 | 1531 | https://github.com/ieee8023/covid-chestxray-dataset |
[58] | RT-PCR | 102 | ** NS | * NA | 235 | Hospital Israelita Albert Einstein, Brazil |
[59] | RT-PCR | ** NS | ** NS | * NA | 53 | Wenzhou Central Hospital and Cangnan People’s Hospital Wenzhou, China |
[60] | Chest CT Images | 924 | 4448 | * NA | 5372 | Renmin Hospital of Wuhan University, Henan Provincial People’s hospital, First Hospital of China Medical University First Affiliated Hospital of Anhui Medical University Beijing Youan Hospital of Capital Medical University, Huangshi Central Hospital, |
[61] | Chest CT Images | 349 | * NA | 463 | 812 | https://www.sirm.org/ https://radiopaedia.org/ https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/ https://coronacases.org/ https://www.eurorad.org/ |
[62] | Chest X-ray CT Images | ** NS | 4273 | 1583 | 5856 | [63] |
[64] | Chest X-ray | 45 | 931 | 660 | 1636 | https://github.com/lindawangg/COVID-Net https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data |
[65] | Chest X-ray | 105 | * NA | 80 | 185 | https://github.com/ieee8023/covid-chestxray-dataset |
[66] | Chest X-ray | 680 | 1845 + 3457 | 9977 | 15,959 | https://www.kaggle.com/c/rsna-pneumonia-detection-challenge https://www.kaggle.com/tawsifurrahman/covid19-radiography-database https://github.com/ieee8023/covid-chestxray-dataset https://github.com/muhammedtalo/COVID-19 |
[67] | Chest X-ray | 534 | 1157 | 1310 | 3001 | https://github.com/ieee8023/covid-chestxray-dataset https://www.kaggle.com/paultimothymooney/chest-Xray |
[68] | Chest X-ray | 210 | 350 | 350 | 910 | https://www.kaggle.com/tawsifurrahman/covid19-radiography-database |
[69] | Chest X-ray | 696 | 696 | 696 | 2088 | https://github.com/ieee8023/covid-chestxray-dataset |
[70] | Clinical Blood Test | Suspected Covid-19 105 | 148 | * NA | 253 | Gansu Provincial Hospital, Lanzhou Pulmonary Hospital, The First Hospital of Lanzhou University, The First People’s Hospital of Lanzhou City, Lanzhou University Second Hospital, China |
[71] | Clinical Blood Test | 160 | 5333 | * NA | 5493 | University Medical Centre Ljubljana (U.M.C.L.), Slovenia |
[72] | Clinical Blood Test | 82 | * NA | 32 | 114 | Taizhou Hospital Zhejiang, China |
Reference | Test Type | ML/DL Techniques | Prediction Results | Country | Cited by No of Papers |
---|---|---|---|---|---|
[26] | CT Images | CNN, COVNet | AUC 0.96 | China | 553 |
[39] | CT Images | CNNs, ResNet-101 & Xception | AUC of 0.99 Sensitivity 98.02% Specificity 99.51% | Iran | 120 |
[41] | CT Images | 3-D DNN, DeCoVNet | Accuracy 0.90 | China | 205 |
[42] | C.T. Images | Inception Transfer learning model establish the algorithm | Accuracy of 89.5% with Specificity of 0.88 and Sensitivity of 0.87 | China | 376 |
[43] | C.T. Images | D.N.N., DRE-Net | A.U.C. of 0.99 Sensitivity of 0.93 | China | 198 |
[49] | CT Images | 2D and 3D deep learning (Resnet-50-2D) and AI Models | AUC of 0.99 Sensitive 92.2% Specificity 92.2% | China | 306 |
[50] | C.T. Images | Classification Stage 1 SVM, Stage 2 GLCM, GLSZ MDWT | Accuracy of 99.68% | Turkey | 103 |
[52] | C.T. Images | Multilayer perceptron and LSTM | AUC of 0.954 | China | 48 |
[54] | CT Images | Combined model 3D UNet++ and RestNet-50 | AUC of 0.991 Sensitivity of 0.974 and specificity of 0.922 | China | 99 |
[60] | C.T. Images | 3D-DNN, COVID-19Net | AUC 0.86 Sensitivity of 79.35% and specificity of 71.43% | China | 95 |
[61] | C.T. Images | CNN, Multi-task learning, self-supervised learning, DenseNet-169 | Accuracy of 0.89 and AUC 0.90 | China | 175 |
[44] | C.T. Images | Five classifiers, Decision tree, k-nearest neighbor, naïve Bayes, support vector machine, Proposed COVIDiag model | Accuracy of 91.4% sensitivity of 93.24%, and specificity of 90.32% | Iran | 7 |
[36] | X-ray Images | CNN, CoroNet | Overall Accuracy 89.6% | India | 159 |
[37] | X-ray Images | CNN VGG16 | Average accuracy 0.97% | Italy | 69 |
[11] | X-ray Images | CNN DarkCovidNet | Accuracy of 98.08% | Turkey | 417 |
[40] | CT Images | 3-D CNN ResNet-18 | Overall Accuracy 86.7% | China | 443 |
[3] | X-ray Images | CNN, MobileNet v2 | Accuracy of 96.78% Sensitive 98.66% Specificity 96.46% | Greece | 480 |
[45] | X-ray Images | D.N.N., VGG-19 ResNet-50, COVID-Net | Accuracy of 93.3% | Canada | 558 |
[46] | X-ray Images | CNN, RestNet50 + SVM | Accuracy of 95.33% | India | 250 |
[47] | X-ray Images | D-CNN, VGG19, DenseNet201 | Classification with F1-scores of 0.89 and 0.91 | Egypt | 251 |
[68] | X-ray Images | CNN, COV19-ResNet, COV19-CNNet | Accuracy of 97.61% | Turkey | 1 |
[48] | X-ray Images | ResNet50, DenseNet201, Inception-v3, and Xception | AUC 0.996, Overall sensitivity of 0.94 | USA | 2 |
[51] | X-ray Images | CNN, Bayesian ResNet50V2 | Accuracy of 89.92% | UK | 104 |
[55] | X-ray Images | DCNN, CheXNet + DenseNet-201 | Accuracy of 99.7% precision, sensitivity of 99.7% and specificity is 99.55% | Qatar | 161 |
[57] | X-ray Images | CNN, Classification Grad-CAM | AUC 95.13%, the sensitivity of 90%, and specificity of 87.84% | China | 161 |
[64] | X-ray Images | CNN, COVID-ResNet | Accuracy of 96.23% | USA | 125 |
[65] | X-ray Images | D-CNN, DeTraC | Accuracy of 95.12% Sensitivity of 97.91% and Specificity of 91.87% | Egypt | 161 |
[66] | X-ray Images | D.N.N., Deep COVID Explainer | Positive Predictive Value 96.12% and recall of 94.3% | Germany | 34 |
[67] | X-ray Images | VGG-16 VGG-19 | Accuracy of 87.49% | Australia | 5 |
[38] | X-ray Images | CNN, AlexNet, GoogleNet, SqueezeNet | Overall accuracy 99% | Saudi Arabia | 4 |
[69] | X-ray Images | Classification Models K.N.N., ANN, D.T., SVM, | Overall accuracy of 93.41% | India | 8 |
[35] | Chest X-ray CT Images | CNN, RestNet50, Inception V3, Inception-RestNetV2 | Accuracy of 98% | Turkey | 408 |
[53] | Chest X-ray CT Images | D-CNN, A.I. system | A.U.C. of 97.91% The sensitivity of 90.19% and specificity of 95.76% | China | 97 |
[56] | Chest X-ray CT Images | Simple 2-D CNN Model and pre-trained AlexNet with transfer learning | Accuracy of 98% on X-ray images and 94.1% on C.T. images | Iraq | 80 |
[62] | Chest X-ray CT Images | D-CNN, Resnet50, MobileNet_V2, Inception_Resnet_V2 | Accuracy of 96.61% | Morocco | 39 |
[58] | RT-PCR | Support Vector Machine, Random Forests, Neural Networks, Logistic Regression | AUC 0.847, Sensitivity 0.67, Specificity 0.85 | Brazil | 31 |
[59] | RT-PCR | Support Vector Machine, K.N.N., Decision Tree, Random Forest | Accuracy of 80% | China | 147 |
[70] | Clinical Blood Test | Random Forests (R.F.) | Accuracy of 0.9512, Sensitivity 0.9697, and Specificity 0.9595 | China | 27 |
[71] | Clinical Blood Test | CRISP-DM, Random Forest, Deep Neural Network, Extreme gradient Boosting Machine (XGBoost) | A.U.C. of 0.97, Sensitivity 81.9%, and specificity of 97.9% | Switzerland | 5 |
[72] | Clinical Blood Test | Six Classification Model BayesNet, logistic-regression, lazy-classifier, meta-classifier, classification via regression, decision-tree (J48) | Accuracy of 84.24% | Turkey | 0 |
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Rehman, A.; Iqbal, M.A.; Xing, H.; Ahmed, I. COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. Appl. Sci. 2021, 11, 3414. https://doi.org/10.3390/app11083414
Rehman A, Iqbal MA, Xing H, Ahmed I. COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. Applied Sciences. 2021; 11(8):3414. https://doi.org/10.3390/app11083414
Chicago/Turabian StyleRehman, Amir, Muhammad Azhar Iqbal, Huanlai Xing, and Irfan Ahmed. 2021. "COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review" Applied Sciences 11, no. 8: 3414. https://doi.org/10.3390/app11083414
APA StyleRehman, A., Iqbal, M. A., Xing, H., & Ahmed, I. (2021). COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. Applied Sciences, 11(8), 3414. https://doi.org/10.3390/app11083414