A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
Abstract
:1. Introduction
- This review provides a more comprehensive review of classification and segmentation method on CT and X-ray datasets of deep learning imaging diagnostic methods for COVID-19.
- This review provides a comprehensive summary of the available COVID-19 datasets.
- The advantages and challenges of deep learning imaging diagnostic methods for COVID-19 are given from multiple perspectives.
2. COVID-19 Datasets
2.1. Main COVID-19 Datasets
2.2. Data Enhancement Methods
3. The Methods for Supervised Learning in Diagnosis of COVID-19
3.1. The Classification Methods of COVID-19 Based on Supervised Learning
3.1.1. The Classification Methods of COVID-19 Based on VGG
3.1.2. The Classification Methods of COVID-19 Based on ResNet
3.1.3. The Classification Methods of COVID-19 Based on DenseNet
3.1.4. The Classification Methods of COVID-19 Based on Lightweight Networks
3.2. The Segmentation Methods of COVID-19 Based on Supervised Learning
3.2.1. The Segmentation Methods of COVID-19 Based on Attention Mechanism
3.2.2. The Segmentation Methods of COVID-19 Based on Multi-scale Mechanism
3.2.3. The Segmentation Methods of COVID-19 Based on Residual Connectivity Mechanism
3.2.4. The Segmentation Methods of COVID-19 Based on Dense Connectivity Mechanism
4. The Methods for Semi-Supervised Learning in Diagnosis of COVID-19
5. The Methods for Unsupervised Learning in Diagnosis of COVID-19
6. Challenges and Future Work
- Dataset size
- Data balance
- Multimodal data
- Aplicability of the model
- Light weight of the model
- Interpretability of the model
- Generalization ability of model
- Small lesions segmentation
- Combine it with other tasks
- Third, detection of new variants of COVID-19
- Fourth, cross disciplinary knowledge
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Datasets Name | Image Modal | Sample Size | Country | Date | Data Format | Online URL |
---|---|---|---|---|---|---|
COVID-chestxray [11] | X-ray | 434 (NCP: 434) | Canada | 2,2020 | JPEG PNG JPG | https://github.com/ieee8023/covid-chestxray-dataset |
QaTa-Cov19 [14] | X-ray | 6286 (BP: 2760, VP: 1485, Normal: 1579, NCP: 462) | Italy Spain China | 5,2020 | / | / |
COVID-CXNet [15] | X-ray | 452 (NCP: 452) | Canada | 6,2020 | JPG PNG | https://github.com/armiro/COVID-CXNet |
Synthetic COVID-19 CXR [16] | X-ray | 21,295 (NCP: 21,295) | / | 10,2020 | JPG | https://github.com/hasibzunair/synthetic-covid-cxr-dataset |
COVIDGR [17] | X-ray | 852 (Normal: 426, NCP: 426) | Spain | 11,2020 | JPG | https://github.com/ari-dasci/OD-covidgr |
COVID-19 radiography database [18] | X-ray | 15,153 (NCP: 3616, VP: 1345, Normal: 10,192) | India | 3,2021 | PNG | https://www.kaggle.com/tawsifurrahman/covid19-radiography-database |
COVID-QU-Ex [19] | X-ray | 13,119 (NCP: 11,956, Pneumonia: 1163) | / | 3,2021 | PNG | https://www.kaggle.com/anasmohammedtahir/covidqu |
COVID19-DB [20] | X-ray | 1559 (NCP: 225, Normal: 1334) | China | 8,2022 | JPG | / |
BrixIA COVID-19 [21] | X-ray | 4703 (NCP: 4703) | Italy | 8,2020 | DICOM | https://github.com/BrixIA/Brixia-score-COVID-19 |
COVIDx-CXR [13] | X-ray | 30,000 (patient: 16,400, X-ray: 30,000) | Canada | 11,2021 | PNG | https://github.com/lindawangg/COVID-Net |
Balanced Augmented COVID CXR Dataset [22] | X-ray | 30,233 (NCP: 8769, LO: 7662, Normal: 8192, VP: 5410) | India | 11,2022 | PNG | https://www.kaggle.com/tr1gg3rtrash/balanced-augmented-covid-cxr-dataset |
COVID-CT [12] | CT | 812 (NCP: 349, N-NCP: 463) | China | 3,2020 | PNG JPG | https://github.com/UCSD-AI4H/COVID-CT |
CC-CCII [23] | CT | 617,775 (NCP, CP, Normal) | China | 4,2020 | JPG PNG | http://ncov-ai.big.ac.cn/download |
COVID-CT-Seg [24] | CT | 20 (NCP: 20) | China | 4,2020 | DICOM | https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark |
Yan [25] | CT | 165,667 (patient: 861, CT: 165,667) | China | 4,2020 | / | / |
MosMedData [26] | CT | 20,685 (patient: 1521, CT: 19,685) | Russia | 4,2020 | NIFIT | https://mosmed.ai/datasets/covid19_1110/ |
SARS-CoV-2 CT-scan [27] | CT | 2482 (NCP: 1252, N-NCP: 1230) | Brazil | 5,2020 | PNG | https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset |
HKBU _HPML_COVID-19 [28] | CT | 340,190 (NCP: 131,517, CP: 135,038, Normal 73,635) | China | 6,2020 | PNG | https://github.com/HKBU-HPML/HKBU_HPML_COVID-19 |
HUST-19 [29] | CT | 19,685 (patient: 1521, CT: 19,685) | China | 8,2020 | DICOM JPEG | http://ictcf.biocuckoo.cn/HUST-19.php |
TCIA [30] | CT | 2724 (patient: 2617, CT: 2724) | China Japan Italy | 8,2020 | / | / |
CC-19 [31] | CT | 34,006 | China | 12,2020 | JPG | https://github.com/abdkhanstd/COVID-19 |
MIDRC-RICORD-1a [32] | CT | 31,856 (NCP: 28,395, N-NCP: 5611) | USA | 12.2020 | DICOM | https://doi.org/10.7937/VTW4-X588 |
COVIDx-CT [13] | CT | 104,009 (Normal: 8066, NCP: 358, Pneumonia 5538) | China | 9,2020 | PNG | https://github.com/lindawangg/COVID-Net |
COVID-Net CT-2 [33] | CT | 200,000 | / | 1,2021 | CKPT | https://github.com/haydengunraj/COVIDNet-CT/blob/master/docs/models.md |
COVIDx CT-2A [13] | CT | 194,922 (CT: 194,922) | China Iran AustraliaEngland | 1,2021 | JPG | https://www.kaggle.com/datasets/hgunraj/covidxct |
COVIDx CT-2B [13] | CT | 201,103 (CT: 201,103) | China Iran AustraliaEngland | 1,2021 | JPG | https://www.kaggle.com/datasets/hgunraj/covidxct |
COVID-CS [34] | CT | 145,167 (NCP: 69,626, N-NCP: 75,541) | China | 2,2021 | JPG | https://github.com/yuhuan-wu/JCS |
COVID-CT-Set [35] | CT | 63,849 (NCP: 15,589, Normal: 48,260) | Iran | 3,2021 | TIFF | https://github.com/mr7495/COVID-CTset |
COVID-CT-MD [36] | CT | 308 (NCP: 160, CP: 69, Normal: 79) | Iran | 4,2021 | DICOM | https://github.com/ShahinSHH/COVID-CT-MD |
Cov-Pne-Bac [37] | CT | 1566 (NCP: 631, VP: 417, CP: 518) | Turkey | 1,2022 | JPG | / |
Large-scale Synthetic COVID-19 CT Dataset [38] | CT | 376,000 | / | 9,2022 | JPG | https://www.kaggle.com/datasets/lee123456789/largescale-synthetic-covid19-ct-dataset |
BIMCV-COVID19+ [39] | X-ray CT | 23,527 (X-ray18 840, CT: 6687) | Spain | 6,2020 | / | https://github.com/BIMCV-CSUSP/BIMCV-COVID-19 |
COVID-19-CT-CXR [40] | X-ray CT | 1590 (CT: 1327, CXR 263) | USA | 11,2020 | / | https://github.com/ncbi-nlp/COVID-19-CT-CXR |
Network Name | Modal | Sample Size | Results (%) | Open Source (Y/N) |
---|---|---|---|---|
VGG19 [47] | X-ray CT | COVID-19: 4320 Pneumonia: 5856 Normal: 20,000 Lung cancer: 3500 | Acc = 98.05 Recall = 98.05 Auc = 99.66 F1 = 98.24 | Y |
VGG [48] | X-ray | COVID-19: 5656 Normal: 5656 | Acc = 96.41 Sen = 96.60 Spe = 96.20 Auc = 97.70 | N |
VGG-16 [49] | X-ray | COVID-19: 816 Pneumonia: 867 Normal: 948 | Acc = 90.00 F1 = 90.00 | N |
Resnet [37] | CT | COVID-19: 631 VP: 417 BP: 518 | Acc = 99.62 | N |
FocusCovid [50] | X-ray | / | Acc = 99.40 | |
ResGANet [51] | CT | COVID-19: 349 Normal: 397 | Acc = 80.00 F1 = 81.00 Auc = 82.00 | N |
ResGNet-C [52] | CT | COVID-19: 148 Normal: 148 | Acc = 96.62 Sen = 97.33 Spe = 95.91 | N |
3D-ResNet [53] | CT | COVID-19: 1315 Pneumonia: 2406 Normal: 936 | Auc = 97.30 | N |
DenseNet-Tiny [54] | X-ray | COVID-19: 1281 Pneumonia: 4657 Normal: 3270 | Acc = 97.99 Pre = 98.38 Recall = 98.15 F1 = 98.26 | Y |
DenseNet [55] | X-ray | COVID-19: 2431 Pneumonia: 1468 Normal: 13,649 | Auc = 94.9 Sen = 90.2 Acc = 80.2 | N |
AM-SdenseNet [56] | CT X-ray | COVID-19:828 Normal:1000 | Acc = 99.18 | Y |
Corona-Nidnna [57] | X-ray | COVID-19: 245 Pneumonia: 5551 Normal: 8066 | Acc = 95.00 Recall = 94.00 | Y |
InceptionV3 [58] | X-ray | COVID-19: 162 Pneumonia: 4280 | Acc = 99.96 | N |
IST-CovNet [1] | CT | COVID-19: 92,905 Pneumonia: 67,712 Normal: 40,030 | Acc = 93.69 | N |
ML-CAM [59] | X-ray CT | COVID-19: 3254 Normal: 2217 | Acc = 94.72 | N |
CNN + CFS [60] | CT | COVID-19: 349 Normal: 397 | Acc = 91.60 Sen = 71.70 Pre = 90.40 F1 = 91.00 | N |
Mechanism | Network Name | Sample Size | Results (%) | Open Source (Y/N) |
---|---|---|---|---|
Attention mechanism | nCoVSegNet [67] | Slices: 244,537 | Dice = 66.80 ESN = 70.70 SPE = 99.75 PPV = 69.77 | Y |
Attention mechanism | CAD CNN [68] | Slices: 393 | Dice = 85.43 Recall = 88.10 | N |
Attention mechanism | D2A U-Net [69] | Slices: 3949 | Dice = 72.98 Recall = 70.71 | N |
Attention mechanism | DUDA-Net [70] | Slices: 557 | Dice = 87.06 Iou = 77.09 Acc = 99.06 Sen = 90.85 | Y |
Attention mechanism | RefNet [71] | Slices: 230 | Dice = 91.37 Sen = 91.54 | N |
Multi-scale mechanism | MSD-Net [72] | Slices: 4780 | Sen = 90.85 Spe = 99.59 | N |
Multi-scale mechanism | MPS-Net [73] | Slices: 300 | Dice = 83.25 Sen = 84.06 Spe = 99.88 Iou = 74.20 | N |
Multi-scale mechanism | [74] | Slices: 3929 | Dice = 83.25 Iou = 74.20 | Y |
Multi-scale mechanism | COVID-SegNet [75] | Slices: 165,667 | Sen = 84.06 Spe = 99.88 | N |
Multi-scale mechanism | JSC [34] | Slices: 2885 | Dice = 78.50 | Y |
Residual connectivity mechanism | ResUnet [76] | Slices: 5349 | Dice = 85.19 Sen = 84.66 Prec = 84.22 | Y |
Residual connectivity mechanism | Backbone + Res_dil + Attention [77] | Slices: 473 | Dice = 83.1 | N |
Residual connectivity mechanism | MultiResUNet [78] | Slices: 3520 | Dice = 74.28 | N |
Residual connectivity mechanism | Literature [79] | Slices: 100 | Dsc = 94 Acc = 89 Pre = 95 | N |
Dense connectivity mechanism | SCOAT-Net [80] | Slices: 17 | DSC = 88.99 SEN = 87.85 PPV = 90.28 | N |
Dense connectivity mechanism | ADID-UNET [81] | Slices: 1318 | Dice = 80.31 Pre = 84.76 Spe = 99.66 Auc = 95.51 | Y |
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Zhou, T.; Liu, F.; Lu, H.; Peng, C.; Ye, X. A Review of Deep Learning Imaging Diagnostic Methods for COVID-19. Electronics 2023, 12, 1167. https://doi.org/10.3390/electronics12051167
Zhou T, Liu F, Lu H, Peng C, Ye X. A Review of Deep Learning Imaging Diagnostic Methods for COVID-19. Electronics. 2023; 12(5):1167. https://doi.org/10.3390/electronics12051167
Chicago/Turabian StyleZhou, Tao, Fengzhen Liu, Huiling Lu, Caiyue Peng, and Xinyu Ye. 2023. "A Review of Deep Learning Imaging Diagnostic Methods for COVID-19" Electronics 12, no. 5: 1167. https://doi.org/10.3390/electronics12051167
APA StyleZhou, T., Liu, F., Lu, H., Peng, C., & Ye, X. (2023). A Review of Deep Learning Imaging Diagnostic Methods for COVID-19. Electronics, 12(5), 1167. https://doi.org/10.3390/electronics12051167