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Quant. Biol.    2022, Vol. 10 Issue (2) : 188-207    https://doi.org/10.15302/J-QB-021-0274
RESEARCH ARTICLE
A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging
Aishwarza Panday1, Muhammad Ashad Kabir2(), Nihad Karim Chowdhury3
1. Department of Computer Science & Engineering, Stamford University, Dhaka 1217, Bangladesh
2. School of Computing and Mathematics, Charles Sturt University, NSW 2795, Australia
3. Department of Computer Science & Engineering, University of Chittagong, Chittagong 4349, Bangladesh
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Abstract

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.

Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.

Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.

Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.
Keywords COVID-19      machine learning      deep learning      detection      classification      diagnosing      X-ray      CT scan     
Corresponding Author(s): Muhammad Ashad Kabir   
Just Accepted Date: 29 November 2021   Online First Date: 18 March 2022    Issue Date: 07 July 2022
 Cite this article:   
Aishwarza Panday,Muhammad Ashad Kabir,Nihad Karim Chowdhury. A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging[J]. Quant. Biol., 2022, 10(2): 188-207.
 URL:  
https://academic.hep.com.cn/qb/EN/10.15302/J-QB-021-0274
https://academic.hep.com.cn/qb/EN/Y2022/V10/I2/188
Fig.1  A semantic representation of the classification of lung diseases.
Title Data source Papers Category Total images
COVID-19 CT segmentation i [14,15] COVID-19: 373 829
COVID-19 database ii [16?18] – –
COVID-CT iii [18?20] COVID-19: 349; Non-COVID: 397 746
Lung segmentation and candidate points generation iv [21,22] COVID-19: 1,252; Non-COVID: 1,230 2,482
LIDC v [23] – 1,018
CC-19 dataset vi [24] COVID-19: 28,395 34,006
Artificial intelligence in radiology vii [18] COVID-19: 1,000 1,000
Chest CT dataset viii [25] COVID-19: 412; Pneumonia: 412; Normal: 412 1,240
Tab.1  Publicly available COVID-19 CT scan datasets
Title Data source Papers Catagory Total
Covid-chestxray-dataset i [21, 30?65] COVID-19: 132 173
non-COVID-19: 41
COVID-19 database ii [30,44,50,56, 66?69] – –
COVID-19 radiography database iii [39,55,56,64,66,70,71] COVID-19: 219 2,905
Normal: 1,341
Pneuomonia: 1,345
COVIDx dataset iv [71] COVID-19: 589 15,493
Normal: 8,851
Pneumonia: 6,053
COVID-19 chest X-ray dataset initiative v [71] – 48
ActualMed COVID-19 Chest X-ray dataset initiative vi [71] – 238
Pneumonia classification vii [72] COVID-19: 90 1,144
Pneumonia: 54
Normal: 1,000
COVID-19 viii [73] COVID-19: 125 1,125
No finding: 500
Pneumonia: 500
COVIDGR-1.0 ix [74] COVID-19: 426 852
Normal: 426
Tubercolosis x [32] COVID-19: 435 2,186
Normal: 439
Pneumonia-bacterial: 439
Pneumonia-viral: 439
Tuberculosis: 434
Threadreader xi [56] COVID-19: 50 50
COVID-19-and-pneumonia-scans xii [75] COVID-19: 199 5,887
Normal: 1,965
Viral Pneumonia: 3,723
COVID-19-X-rays xiii [59] – 95
Tab.2  Publicly available COVID-19 X-ray image datasets
Fig.2  Different types of pre-processing techniques
Preprocessing methods Papers
Resize [4, 14, 16,18,19, 25,30,33,35?38,41,43,45,47,49,55,56,58,60?62,65?68, 70, 73,79?88]
Flipping or rotating [14?16, 22, 25, 27, 28, 30,34, 37, 39, 43?47,51, 54,56,60,61,65?68,71,73,77, 83, 86?98 ]
Scaling or cropping [4, 14,15, 16,18,19,24,25,27,35,37,39,43,46,47,51,55,59,60,62,65,71,83?85,92,94,95,97]
Contrast adjustment [27,43,66,67,70,76,78,80,81,83,90,91,95,96,99,100]
Brightness or intensity adjustment [16,27,43,51,54,71,78,87,94,95]
GAN [19,74,88,96,97,101]
Tab.3  Different types of preprocessing methods used by the papers
Fig.3  Ratio of different pre-processing methods.
Fig.4  Basic deep learning architecture.
CNN Papers
ResNet [16,18, 20, 22?24, 26?28, 82,85,92,105,106]
VGG [107, 16, 22,24,26,83,85,92, 105,107]
DenseNet [20,22, 24,26, 27,105]
MobileNet [20,24,26,85]
SqueezeNet [17,28,85]
Inception [20,100]
CrNet [105]
EfficientNet [25,95,105]
GoogLeNet [16,85]
InceptionResNet [22,26]
NasNetMobile [26]
Alex-Net [24,85]
Xception [85]
Tab.4  Feature extraction methods for CT images used by the papers
CNN Papers
ResNet [26,32,33,35?37, 40?42,44, 47?49,51,53,54,56,57,59,60,62,65,68,74,76,77,80,87,89,91,93,94,96,98,101,111]
DenseNet [26,32,35,37,38,40,43,48,49,52,60,65, 76?78,80,81,84,87,94,97,98, 111]
VGG [4,26,32,33,42,44,48,49,51,55?57, 59,60,65,73,75,77,80,81,84,86,89,97,112]
Inception [21,35,48,49,59,60,62,63,66,72,76, 84,88, 96, 97, 111]
Xception [32?34,45, 47?49,59,65, 81, 84, 97]
InceptionResNet [26,32, 35,37,48,49,56,59,60,81,84]
NasNet [26,32,37,38,49,81,97]
AlexNet [32,48,59,80,89,101]
GoogLeNet [32,42,48,59,89,101]
SqueezeNet [56,64,76,78,87,93,98,101]
ShuffleNet [32,48,78]
MobileNet [26,32,48,56,59,60,64,65,67,69,81]
EfficientNet [51]
CheXNet [90]
CapsNet [46,61]
Tab.5  Feature extraction methods for X-ray images used by the papers
Custom CNN Interpretability method Dataset type Classification Accuracy (%)
PDCOVIDNet [30] Grad-CAM & Grad-CAM++ X-ray Multi-class 96.58
CovMUNET [31] – X-ray Multi-class 99.41
COVID-SDNet [74] Grad-CAM X-ray Binary 97.37
COVID-Net [71] Grad-CAM X-ray Multi-class 93.30
CovXNet [79] Grad-CAM X-ray Multi-class 97.40
CoroNet [45] – X-ray Binary 89.60
COVID-CAPS [46] – X-ray Binary 95.90
DECAPS [19] – CT scan Binary 87.60
DarkCovidNet [57] Grad-CAM X-ray Multi-class 98.08
Convolutional capsnet [61] – X-ray Binary 97.24
Tab.6  Summary of custom CNN architectures for COVID-19 detection
Classification strategies Papers
Binary class [4,15?17,19,20,22,24?28,34,35,37?39,41,43,44?46,48,49,52,55,58,61,63,69,74,78,80,81,83,85,86,89,90,92,93,95,99?101,105,107,111,113-115]
Multi-class [14,18,21,23,30?33,36,40,42,47,48,50,51,53,54,56,57,59,60,62,64?68,70?73,75?77,79,82,84,87,88,91,94,96?98,106,112]
Tab.7  Classification strategies followed by different methods
Paper Total images Train,val,test ratio (%) Train Val Test Result (%) Model name
[14] 373 80,–,20 300 – 73 99.97 (Se) cGAN
[26] 400 80,–,20 329 – 80 95.20 (A) NasNetMobile
[24] 34,006 – – – – 96.70 (Se) Inception
[107] 4,447 45,5,50 2,000 222 2,225 97 (Se) VGG
[23] 10,250 – – – – 90.19 (Se) ResNet
[27] 812 42,18,40 341 146 325 79.50 (A) DenseNet
[20] 746 52,14,25 425 118 203 90.61 (A) DenseNet
[113] 2,522 – – – – 97.79 (A) AFS-DF
[28] 746 55,23,22 410 172 164 99.40 (A) ResNet
[82] 1,865 Random split 1,725 270 320 99.40 (AUC) ResNet
[16] 6,000 75,–,25 4,500 – 1,500 98.27 (A) Feature fusion (VGG+ GoogLeNet+ResNet)
[21] 420 45,45,10 189 189 42 99.56 (A) IRRCNN
[83] 360 80,10,10 288 36 36 89.20 (A) VGG
[92] 3,855 – – – – 95.00 (Se) ResNet
[105] 746 57,15,28 425 119 208 83.00 (A) DenseNet
[99] 470 60,–,40 282 – 188 93.65 (A) FFT-Gabor
[19] 746 85,–,15 634 – 112 87.60 (A) Decapse
[114] 1,044 80,10,10 835 104 105 86.00 (A) U-Net
[15] 100 60,–,40 60 – 40 83.62 (A) MiniSeg
[100] 453 – – – – 89.50 (A) Inception
[17] 783 56,26,18 438 203 145 83.00 (A) SqueezeNet
[18] 2,200 – – – – 91.00 (A) ResNet
[106] 4,352 90,–,10 3,916 – 436 98.00 (A) ResNet
[85] 1,020 80,10,10 816 102 102 99.4 (A) ResNet
[25] 828 80,10,10 662 83 83 96.2 (AUC) EfficientNet
[95] 132,583 70,10,20 92,808 13,258 26,517 96.00 (A) EfficientNet
[115] 538 – – – – 97.00 (AUC) U-Net
[22] 2,492 68,17,15 1,694 424 374 97.00 (AUC) DenseNet
Tab.8  Summary of the classification results for CT images
Evaluation criteria Papers Total (%)
Detection only [16,20,22,25,27,45,85,113] 8 (29%)
Abnormality localization [14,15,21,24,28,83,92,107,114] 9 (32%)
Visual interpretation [17,18,26,82,105] 5 (18%)
Verify by radiologists [19,23,95, 100,106,115] 6 (21%)
Tab.9  Different evaluation criteria used in CT scan based COVID-19 detection research
Paper Total images Train, val, test (%) Train Val Test Result (%) Model name
[30] 2,905 80, 10, 10 2,324 291 291 96.58 (A) PDCOVIDNet
[31] 6,594 5-fold C-V – – – 99.41 (A) CovMUNET
[26] 400 80,–,20 320 – 80 100 (A) NasNetMobile
[70] 9,521 10-fold C-V – – – 95.00 (F1) COV-ELM
[32] 2,186 5-fold C-V – – – 98.00 (A) ResNet
[33] 375 80, 10, 10 300 37 38 97.30 (A) VGG16
[74] 754 80, 20, – 603 150 – 97.37 (A) ResNet
[111] 5,824 80,–,20 4,659 – 1,165 99.49 (A) DenseNet
[34] 1,419 80,–,20 1,135 – 284 98.94 (A) Xception
[73] 6,523 30,40,30 2,000 2,523 2,000 98.00 (A) VGG
[35] 1,302 60,–,40 781 – 521 85.10 (Se) DenseNet
[36] 15,282 90,–,10 13,703 – 1,579 98.06 (A) ResNet
[37] 1,214 4-fold C-V – – – 98.00 (A) NASNet-Large
[38] 3,309 80,–,20 2,647 – 662 100 (Se) NASNet-Large
[66] 35,500 88,8,4 31,340 2,360 1,800 98.00 (A) Inception
[39] 1,312 70,20,10 918 131 263 97.40 (A) CAD system
[79] 6,161 5-fold C-V – – – 97.40 (A) CovXNet
[89] 1,864 – – – – 97.54 (A) ResNet
[80] 2,271 70,–,30 1,590 – 681 98.90 (A) DenseNet
[81] 239 70,10,20 167 22 20 98.00 (A) Residual Att. Net
[40] 6,297 27,46,26 1,591 2,772 1,439 97.10 (A) DenseNet
[41] 502 70,10,20 399 3 100 88.90 (A) ResNet
[72] 1,144 70,–,30 801 – 343 89.60 (F1) Inception
[90] 6,286 80,–,20 5,029 – 1,257 87.07 (A) CSEN
[71] 13,975 – – – – 93.30 (A) COVID-Net
[91] 1,764 70,–,30 1,235 – 530 95.12 (A) ResNet
[42] 2,239 – – – – 97.01 (A) DCSL
[76] 701 80,–,20 561 – 140 98.22 (A) Inception
[67] 3,905 10-fold C-V – – – 99.18(A) MobileNet
[101] 5,824 80,–,20 4,659 – 1,165 99.00 (A) Resnet
[77] 16,995 5-fold C-V – – – 94.80 (A) DenseNet
[43] 414 70,15,15 290 62 62 98.00 (A) DenseNet
[21] 5,216 80,10,10 4,172 522 522 94.52 (A) IRRCNN and NABLA-3
[44] 455 10-fold C-V – – – 91.24 (A) ResNet50+VGG16+CNN
[78] 537 70,10,20 376 54 107 93.50 (A) MobileNetv2
[45] 1,300 4-fold C-V – – – 89.60 (A) Xception
[84] 16,700 90,–,10 15,030 – 1,670 99.01 (A) Inception
[46] 864 90,10,– 777 87 – 95.70 (A) COVID-CAPS
[47] 15,085 5-fold C-V – – – 91.40 (A) Xception+ResNet50V2
[93] 5,071 40,–,60 2,028 – 30,426 97.50 (Se) ResNet
[48] 381 60,20,20 228 76 76 95.33 (A) ResNet
[49] 274 10-fold C-V – – – 99.00 (A) DenseNet
[50] 1,277 5-fold C-V – – – 90.13 (A) VGG
[51] 13,800 98,–,2 13,525 – 276 93.90 (A) EfficientNet
[55] 59,937 80,–,20 47,950 – 11,987 88.04(AUC) DenseNet
[56] 11,663 91,–,9 10,613 – 1,050 88.33 (Se) ResNet
[94] 15,111 80,10,10 12,088 1,511 1,511 89.40 (A) DenseNet
[54] 18,567 90,–,10 16,714 – 1,853 96.10 (A) ResNet
[55] 1,124 83,–,17 932 – 192 95.00 (A) VGG
[68] 1,122 92,4,4 1,052 35 35 72.38 (A) ResNet
[56] 3,487 72,8,20 2,510 279 698 72.38 (A) ResNet
[57] 3487 80,20,– 900 225 – 98.08 (A) DarkCovidNet
[58] 364 65,15,20 233 56 75 96.30 (A) VGG
[75] 396 80,–,20 316 – 80 100 (Se) VGG
[59] 1,651 10-fold C-V – – – 98.75 (A) VGG
[60] 6,086 80,–,20 4,869 – 1,217 92.18 (A) Inception-ResNetV2
[69] 3,451 80,–,20 2,761 – 690 98.01 (A) FrMEMs
[61] 1,281 10-fold C-V – – – 97.24 (A) Caps-Net
[86] 284 70,–,30 199 – 85 97.97 (A) VGG
[96] 337 – – – – 89.00 (A) ResNet
[97] 15,199 80,–,20 12,160 – 3,039 93.00 (A) VGG
[62] 6,845 10-fold C-V – – – 99.96 (A) Inception
[98] 1,725 80,–,20 1,390 – 335 98.00 (A) ResNet
[87] 5,949 90,–,10 5,310 – 639 98.30 (A) SquzeeNet
[63] 50 – – – – 98.99 (A) Inception
[64] 458 70,–,30 321 – 137 99.27 (A) MobileNetV2 +SqueezeNet
[65] 678 60,20,20 406 136 136 98.15 (A) ResNet
[112] 1,500 5-fold C-V – – – 94.93 (A) VGG
[88] 572 5-fold C-V – – – 100 (AUC) Inception
[4] 8,474 90,–,10 7,626 – 847 98.60 (A) VGG
Tab.10  Summary of the classification results for X-rays
Evaluation criteria Papers Total (%)
Detection only [4,32,34,35,38,41?49, 54?56,58?70,72,75,78,80,81, 86,87,89?91,94,96,98,101,112] 45 (64%)
Abnormality localization [21,31,39,52,73,76,79] 7 (10%)
Visual interpretation [26,30,33,36,37,40,50?53,71,77,84,97] 13 (19%)
Verify by radiologists [57,74,88,93,111] 5 (7%)
Tab.11  Different evaluation criteria used in X-ray based COVID-19 detection research
Fig.5  Chest X-ray images of a patent in three points in the upper row and in a lower row their heat maps using Grad-CAM.
Interpretability method Papers
Grad-CAM [18,23,82,105,106]
CAM [17,83]
LIME [26]
Tab.12  Interpretability methods used in the CT images based works
Interpretabilitymethod Papers
Grad-CAM [31,34,37,38,44,45,53,55,56,63,68,73,76,77,79,84,97]
Grad-CAM++ [31,34,40]
CAM [30,37,54,55,60,87,88,94]
LIME [14,41,68]
LRP [31]
Tab.13  Interpretability methods used in the X-ray images based works
Fig.6  Visual explanation studies that used machine learning techniques in chest X-rays and CT scans.
Database Query string/keywords
Scopus (TITLE-ABS-KEY ((“COVID-19” OR “coronavirus” OR “corona virus” OR “coron- aviruses” OR “2019-nCoV” OR “SARS-CoV” OR “MERS-CoV” OR “severe acute respiratory syndrome” OR “middle east respiratory syndrome”) AND (“X-ray” OR “CT-scan”)) AND TITLE-ABS-KEY ((“deep learning” OR “machine learning” OR “artificial intelligence”)))
Web of science TS= (( COVID-19 OR coronavirus OR Corona virus OR coronaviruses OR 2019-nCoV OR SARS-CoV OR MERS-CoV OR Severe acute respiratory syndrome OR middle east respiratory syndrome) AND (X-ray OR CT-scan) AND (deep learning OR machine learning OR artificial intelligence))
Google scholar COVID-19, X-ray, CT-scan, machine learning, deep learning, artificial intelligence
Tab.14  Keywords used in database search
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