A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
Abstract
:1. Introduction
2. Methodology
3. The Basic Process to Apply Deep Learning for Lung Disease Detection
3.1. Image Acquisition Phase
3.2. Preprocessing Phase
3.3. Training Phase
3.4. Classification Phase
4. The Taxonomy of State-Of-The-Art Work on Lung Disease Detection Using Deep Learning
4.1. Image Type
4.1.1. Chest X-rays
4.1.2. CT Scans
4.1.3. Sputum Smear Microscopy Images
4.1.4. Histopathology Images
4.2. Features
4.3. Data Augmentation
4.4. Types of Deep Learning Algorithm
4.5. Transfer Learning
4.6. Ensemble of Classifiers
4.7. Type of Disease
4.7.1. Tuberculosis
4.7.2. Pneumonia
4.7.3. Lung Cancer
4.7.4. COVID-19
4.8. Dataset
5. Analysis of Trend, Issues and Future Directions of Lung Disease Detection Using Deep Learning
5.1. An Analysis of the Trend of Lung Disease Detection in Recent Years
5.1.1. Trend Analysis of the Image Type Used
5.1.2. Trend Analysis of the Features Used
5.1.3. Trend Analysis of the Usage of Data Augmentation
5.1.4. Trend Analysis of the Types of Deep Learning Algorithm Used
5.1.5. Trend Analysis of the Usage Of Transfer Learning
5.1.6. Trend Analysis of the Usage Of Ensemble
5.1.7. Trend Analysis of the Type Of Lung Disease Detected using Deep Learning
5.2. Issues and Future Direction of Lung Disease Detection Using Deep Learning
5.2.1. Issues
- (i)
- Data imbalance: When doing classification training, if the number of samples of one class is a lot higher than the other class, the resulting model would be biased. It is better to have the same number of images in each class. However, oftentimes that is not the case. For example, when performing a multiclass classification of COVID-19, pneumonia and normal lungs, the number of images for pneumonia far exceeds the number of images for COVID-19 [126].
- (ii)
- Handling of huge image size: Most researchers reduced the original image size during training to reduce computational cost. It is extremely computationally expensive to train with the original image size, and it is also time-consuming to train a deeply complex model even with the aid of the most powerful GPU hardware.
- (iii)
- Limited available datasets: Ideally, thousands of images of each class should be obtained for training. This is to produce a more accurate classifier. However, due to the limited number of datasets, the number of available training data is often less than ideal. This causes researchers to search for other alternatives to produce a good classifier.
- (iv)
- High correlation of errors when using ensemble techniques: It requires a variety of errors for an ensemble of classifiers to perform the best. The base classifiers used should have a very low correlation. This, in turn, will ensure the errors of those classifiers also will be varied. In other words, it is expected that the base classifiers will complement each other to produce better classification results. Most of the studies surveyed only combine classifiers that were trained on similar features. This causes the correlation error of the base classifiers to be high.
5.2.2. Potential Future Works
- (i)
- Make datasets available to the public: Some researchers used private hospital datasets. To obtain larger datasets, efforts such as de-identification of confidential patients’ information can be conducted to make the data public. With more data available, the produced classifiers would be more accurate. This is because, with more data comes more diversity. This decreases the generalisation error because the model becomes more general as it was trained on more examples. Medical data are hard to come by. Therefore, if the datasets were made public, more data would be available for researchers.
- (ii)
- Usage of cloud computing: Performing training using cloud computing might overcome the problem of handling of huge image size. On a local mid-range computer, training with large images will be slow. A high-end computer might speed up the process a little, but it might still be infeasible. However, by training the deep learning model using cloud computing, we can use multiple GPUs at a reasonable cost. This allows higher computational cost training to be conducted faster and cheaper.
- (iii)
- Usage of more variety of features: Most researchers use features automatically extracted by CNN. Some other features such as SIFT, GIST, Gabor, LBP and HOG were studied. However, many other features are still yet to be explored, for example quadtree and image histogram. Efforts can be directed to studying different types of features. This can address the issue of the high correlation of errors when using ensemble techniques. With more features comes more variation. When combining many variations, the results are often better [41]. Feature engineering allows the extraction of more information from present data. New information is extracted in terms of new features. These features might have a better ability to describe the variance in the training data, thus improving model accuracy.
- (iv)
- Usage of the ensemble learning: Ensemble techniques show great potentials. Ensemble methods often improve detection accuracy. An ensemble of several features might provide better detection results. An ensemble of different deep learning techniques could also be considered because ensembles perform better if the errors of the base classifiers have a low correlation.
6. Limitation of the Survey
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Authors | Deep Learning Technique | Features | Dataset |
---|---|---|---|
[74] | CNN with transfer learning and data augmentation | Features extracted from CNN | Montgomery |
[38] | K-nearest neighbour, Simple Linear Regression and Sequential Minimal Optimisation (SMO) Classification | Area, major axis, minor axis, eccentricity, mean, kurtosis, skewness and entropy | Shenzhen |
[84] | ViDi | Features extracted from CNN | Unspecified |
[64] | CNN | Gabor, LBP, SIFT, PHOG and Features extracted from CNN | Private dataset |
[24] | CNN | Features extracted from CNN | ImageCLEF 2018 dataset |
[62] | CNN with transfer learning, with demographic information | Features extracted from CNN + demographic information | Private dataset |
[79] | CNN with data augmentation, and ensemble by weighted averages of probability scores | Features extracted from CNN | Montgomery, Shenzhen, Belarus, JSRT |
[70] | CNN with transfer learning and data augmentation | Features extracted from CNN | Private dataset, Montgomery, Shenzhen |
[69] | CNN | Features extracted from CNN | Private datasets, Montgomery, Shenzhen |
[71] | CNN with transfer learning and ensemble by simple linear probabilities averaging | Features extracted from CNN + rule-based features | Indiana, JSRT, Shenzhen |
[29] | CNN | HoG features | ZiehlNeelsen Sputum smear Microscopy image DataBase |
[75] | CNN and shuffle sampling | Features extracted from CNN | Private datasets |
[81] | CNN with transfer learning and ensemble by averaging | CNN extracted features from edge images | Montgomery, Shenzhen |
[57] | CNN with transfer learning, data augmentation and ensemble by weighted probability scores average | Features extracted from CNN | Private dataset, Montgomery, Shenzhen, Belarus |
[85] | AutoEncoder-CNN | Features extracted from CNN | Private dataset |
[76] | CNN with transfer learning and shuffle sampling | Features extracted from CNN | Private dataset |
[65] | End-to-end CNN | Features extracted from CNN | Montgomery, Shenzhen |
[88] | Optical flow model | Activity Description Vector on optical flow of video sequences | ImageCLEF 2019 dataset |
[28] | CNN | Colours | TBimages dataset |
[83] | Modified maximum pattern margin support vector machine (modified miSVM) | First four moments of the intensity distributions | Private datasets |
[61] | CAD4TB with clinical information | Features extracted from CNN + clinical features | Private dataset |
[31] | DBN | LoH + SURF features | ZiehlNeelsen Sputum smear Microscopy image DataBase |
[60] | CAD4TB | Features extracted from CNN | Private dataset |
[72] | CNN with transfer learning and data augmentation | Features extracted from CNN | Montgomery, Shenzhen, NIH-14 dataset |
[30] | CNN | Features extracted from CNN | TBimages dataset |
[63] | CNN from scratch and data augmentation | Features extracted from CNN | Montgomery, Shenzhen, Belarus |
[86] | 3D CNN | Features extracted from CNN + lung volume + patient attribute metadata | ImageCLEF 2019 dataset |
[12] | CNN with transfer learning and ensemble by stacking | local and global feature descriptors + features extracted from CNN | Private dataset, Montgomery, Shenzhen, India |
[80] | CNN with transfer learning and feature level ensemble | Features extracted from CNN | Shenzhen |
[15] | CNN with transfer learning and ensemble by averaging | CNN extracted features from edge images | Montgomery, Shenzhen |
[32] | CNN with transfer learning | Features extracted from CNN | ZiehlNeelsen Sputum smear Microscopy image DataBase |
[66] | CNN with data augmentation | Features extracted from CNN | Shenzhen |
[73] | CNN with transfer learning and data augmentation | Features extracted from CNN | NIH-14, Montgomery, Shenzhen |
[19] | CNN with transfer learning, Bag of CNN Features and ensemble by a simple soft-voting scheme | Features extracted from CNN + BOW | Private dataset, Montgomery, Shenzhen |
[36] | Neural network | Shape, curvature descriptor histograms, eigenvalues of Hessian matrix | Montgomery, Shenzhen |
[77] | CNN with transfer learning and data augmentation | Features extracted from CNN | Montgomery, Shenzhen, NIH-14 |
[87] | 3D CNN | Features extracted from CNN | ImageCLEF 2019 dataset |
[78] | CNN and Artificial Ecosystem-based Optimisation algorithm | Features extracted from CNN | Shenzhen |
[67] | CNN | Features extracted from CNN | Shenzhen |
[68] | Bayesian based CNN | Features extracted from CNN | Montgomery, Shenzhen |
[82] | CNN with transfer learning, and ensemble by majority voting, simple averaging, weighted averaging, and stacking | Features extracted from CNN | Montgomery, Shenzhen, LDOCTCXR, 2018 RSNA pneumonia challenge dataset, Indiana dataset |
Reference | Deep Learning Technique | Features | Dataset |
---|---|---|---|
[99] | Deep Siamese based neural network | CNN extracted features from the left half and right half of the lungs | Unspecified Kaggle dataset |
[20] | CNN with transfer learning and data augmentation | Features extracted from CNN | LDOCTCXR |
[55] | CNN with transfer learning, data augmentation and ensemble by majority voting. | Features extracted from CNN | LDOCTCXR |
[93] | CNN with transfer learning | Features extracted from CNN | LDOCTCXR |
[102] | CNN with transfer learning, data augmentation and ensemble by combining confidence scores and bounding boxes. | Features extracted from CNN | Radiological Society of North America (RSNA) pneumonia dataset |
[96] | CNN with transfer learning and data augmentation | Features extracted from CNN | NIH Chest X-ray Dataset |
[92] | CNN from scratch and data augmentation | Features extracted from CNN | LDOCTCXR |
[95] | CNN with transfer learning | Features extracted from CNN | LDOCTCXR |
[91] | CNN | Features extracted from CNN | Mooney’s Kaggle dataset |
[100] | CNN and LSTM-CNN, with transfer learning and data augmentation | Features extracted from CNN | Mooney’s Kaggle dataset |
[103] | CNN with probabilistic map of pneumonia | Features extracted from CNN | 2018 RSNA pneumonia challenge dataset |
[101] | Decision Tree, Random Forest, K-nearest neighbour, AdaBoost, Gradient Boost, XGBboost, CNN | Multiple features | Mooney’s Kaggle dataset |
[98] | CNN with transfer learning, data augmentation and ensemble by weighted averaging | Features extracted from CNN | LDOCTCXR |
[97] | CNN with transfer learning and data augmentation | Features extracted from CNN | Mooney’s Kaggle dataset |
[94] | CNN with transfer learning | Features extracted from CNN | Private dataset |
Reference | Deep Learning Technique | Features | Dataset |
---|---|---|---|
[13] | CNN | Features extracted from CNN | LUNA, LIDC, NLST |
[113] | CNN with transfer learning | Features extracted from CNN | JSRT Dataset, NIH-14 dataset |
[107] | Multi-stream multi-scale convolutional networks | Features extracted from CNN | MILD dataset DLCST dataset |
[34] | CNN with transfer learning | Features extracted from CNN | NCI Genomic Data Commons |
[110] | CNN with transfer learning and data augmentation | Features extracted from CNN | NSCLC-Radiomics, NSCLC-Radiomics-Genomics, RIDER Collections and several private datasets |
[105] | CNN and DBN | Features extracted from CNN and DBN | LIDC-IDRI |
[112] | CNN with transfer learning | Features extracted from CNN | Kaggle Data Science Bowl 2017 dataset, Lung Nodule Analysis 2016 (LUNA16) dataset |
[25] | CNN | Features extracted from CNN | LIDC-IDRI |
[108] | CNN | Features extracted from CNN | LIDC-IDRI |
[23] | CNN with data augmentation | Features extracted from CNN | LIDC-IDRI database |
[111] | CNN with transfer learning and data augmentation | Features extracted from CNN | Private dataset |
[14] | Bone elimination and lung segmentation before training with CNN | Features extracted using CNN from bone eliminated lung images and segmented lung images | JSRT dataset |
[114] | CNN-long short-term memory network | Features extracted from CNN | NIH-14 dataset |
[109] | CNN with transfer learning and data augmentation | Features extracted from CNN | JSRT database |
[106] | CNN with data augmentation | Features extracted from CNN | Cancer Imaging Archive |
Authors | Deep Learning Technique | Features | Dataset |
---|---|---|---|
[137] | CNN with transfer learning and location-attention classification mechanism | Features extracted from CNN | Private dataset |
[125] | CNN with transfer learning and data augmentation | Features extracted from CNN | SIRM database, Cohen’s Github dataset, Chowdhury’s Kaggle dataset |
[26] | RADLogics Inc., CNN with transfer learning and data augmentation | Features extracted from RADLogics Inc and CNN | Chainz Dataset, A dataset from a hospital in Wenzhou, China, Dataset from El-Camino Hospital (CA) and Lung image database consortium (LIDC) |
[123] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github dataset and LDOCTCXR |
[21] | CNN with transfer learning and data augmentation | Features extracted from CNN | Cohen’s Github dataset and unspecified Kaggle dataset |
[135] | VB-Net and modified random decision forests method | 96 handcrafted image features | Dataset obtained from Tongji Hospital of Huazhong University of Science and Technology, Shanghai Public Health Clinical Center of Fudan University, and China-Japan Union Hospital of Jilin University. |
[126] | CNN from scratch and data augmentation | Features extracted from CNN | COVIDx Dataset |
[127] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github dataset, Andrew’s Kaggle dataset, LDOCTCXR |
[117] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github dataset, RSNA pneumonia dataset, COVIDx |
[131] | CNN with transfer learning and data augmentation | Features extracted from CNN | Sajid’s Kaggle dataset |
[4] | CNN with transfer learning and data augmentation | Features extracted from CNN | Cohen’s Github dataset, Mooney’s Kaggle dataset |
[118] | CNN with transfer learning | Features extracted from CNN | COVID-CT-Dataset |
[128] | CNN as feature extractor and long short-term memory (LSTM) network as classifier | Features extracted from CNN | GitHub, Radiopaedia, The Cancer Imaging Archive, SIRM, Kaggle repository, NIH dataset, Mendeley dataset |
[132] | CNN with transfer learning and synthetic data generation and augmentation | Features extracted from CNN | Cohen’s Github, Chowdhury’s Kaggle dataset, COVID-19 Chest X-ray Dataset, Initiative |
[129] | CNN with transfer learning, data augmentation and ensemble by majority voting | Features extracted from CNN | Cohen’s Github, LDOCTCXR |
[134] | CNN with transfer learning and stacking ensemble | Features extracted from CNN | Private dataset, LDOCTCXR |
[130] | CNN | Features extracted from CNN | Private dataset |
[138] | Multi-objective differential evolution-based CNN | Features extracted from CNN | Unspecified |
[119] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github |
[139] | CNN and ConvLSTM with data augmentation | Features extracted from CNN | Cohen’s Github, COVID-CT-Dataset |
[120] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github |
[133] | CNN with ensemble by weighted averaging | Features extracted from CNN | Private hospital datasets |
[121] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github, Mooney’s Kaggle dataset, Shenzhen and Montgomery datasets |
[140] | MLP-CNN based model | Features extracted from CNN | Cohen’s Github |
[122] | CNN with transfer learning | Features extracted from CNN | Cohen’s Github, unspecified Kaggle dataset |
[141] | Capsule Network-based framework with transfer learning | Features extracted from CNN | Cohen’s Github, Mooney’s Kaggle dataset |
Name | Disease | Image Type | Reference | Number of Images | Link |
---|---|---|---|---|---|
Belarus dataset | Tuberculosis | X-ray and CT | [142] | 1299 | http://tuberculosis.by |
ImageCLEF 2018 dataset | Tuberculosis | CT | 2287 | https://www.imageclef.org/2018/tuberculosis | |
ImageCLEF 2019 dataset | Tuberculosis | CT | [143] | 335 | https://www.imageclef.org/2019/medical/tuberculosis |
India | Tuberculosis | X-ray | [39] | 78 tuberculosis and 78 normal | https://sourceforge.net/projects/tbxpredict/ |
Indiana Dataset | Multiple diseases with annotations | X-ray | [144] | 7284 | https://openi.nlm.nih.gov |
JSRT dataset | Lung nodules and normal | X-ray and CT | [145] | 154 nodule and 93 non-nodule | http://db.jsrt.or.jp/eng.php |
Montgomery and Shenzhen datasets | Tuberculosis and normal | X-ray | [146] | 394 tuberculosis and 384 normal | https://lhncbc.nlm.nih.gov/publication/pub9931 |
NIH-14 dataset | Pneumonia and 13 other diseases | X-ray | [147] | 112120 | https://www.kaggle.com/nih-chest-xrays/data |
TBimages dataset | Tuberculosis | Sputum smear microscopy image | [148] | 1320 | http://www.tbimages.ufam.edu.br/ |
ZiehlNeelsen Sputum smear Microscopy image DataBase | Tuberculosis | Sputum smear microscopy image | [27] | 620 tuberculosis and 622 normal | http://14.139.240.55/znsm/ |
Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images (LDOCTCXR) | Pneumonia and normal | X-ray | [93] | 3883 pneumonia and 1349 normal | https://data.mendeley.com/datasets/rscbjbr9sj/3 |
Radiological Society of North America (RSNA) pneumonia dataset | Pneumonia and normal | X-ray | 5528 | https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data |
Name | Disease | Image Type | Reference | Number of Images | Link |
---|---|---|---|---|---|
LDOCTCXR | X-ray | [93] | 3883 pneumonia and 1349 normal | https://data.mendeley.com/datasets/rscbjbr9sj/3 | |
NIH Chest X-ray Dataset | Pneumonia and 13 other diseases | X-ray | [147] | 112,120 | https://www.kaggle.com/nih-chest-xrays/data |
Radiological Society of North America (RSNA) pneumonia dataset | Pneumonia and normal | X-ray | 5528 | https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data | |
Mooney’s Kaggle dataset | Pneumonia and normal | X-ray | 5863 | https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia |
Name | Disease | Image Type | Reference | Number of Images | Link |
---|---|---|---|---|---|
JSRT dataset | Lung nodules and normal lungs | X-ray and CT | [145] | 154 nodule and 93 non-nodule | http://db.jsrt.or.jp/eng.php |
Kaggle Data Science Bowl 2017 dataset | Lung Cancer | CT scans | 601 | https://www.kaggle.com/c/data-science-bowl-2017/overview | |
LIDC-IDRI | Lung Cancer | CT | [149] | 1018 | https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI |
Lung Nodule Analysis 2016 (LUNA16) dataset | Location and size of lung nodules | CT scans | [8] | 888 | https://luna16.grand-challenge.org/download/ |
NCI Genomic Data Commons | Lung Cancer | histopa- thology images | [150] | More than 575,000 | https://portal.gdc.cancer.gov/ |
NIH-14 dataset | 14 lung diseases | X-ray | [147] | 112,120 | https://www.kaggle.com/nih-chest-xrays/data |
NLST | Lung Cancer | CT | Approximately 200,000 | https://biometry.nci.nih.gov/cdas/learn/nlst/images/ | |
NSCLC-Radiomics | Lung Cancer | CT | 422 | https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics | |
NSCLC- Radiomics -Genomics | Lung Cancer | CT | 89 | https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics-Genomics | |
RIDER Collections | Lung Cancer | CT | Approximately 280,000 | https://wiki.cancerimagingarchive.net/display/Public/RIDER+Collections |
Name | Disease | Image Type | Reference | Number of Images | Link |
---|---|---|---|---|---|
Andrew’s Kaggle dataset | COVID-19 | X-ray and CT | 79 | https://www.kaggle.com/andrewmvd/convid19-x-rays | |
Chainz Dataset | COVID-19 and normal | CT | 50 COVID-19, 51 normal | www.ChainZ.cn | |
Chowdhury’s Kaggle dataset | COVID-19, normal and pneumonia | X-ray | [125] | 219 COVID-19, 1341 normal and 1345 pneumonia | https://www.kaggle.com/tawsifurrahman/covid19-radiography-database |
Cohen’s Github dataset | COVID-19 | X-ray and CT | [151] | 123 | https://github.com/ieee8023/covid-chestxray-dataset |
COVIDx Dataset | COVID-19, normal and pneumonia | X-ray | [126] | 573 COVID-19, 8066 normal and 5559 pneumonia | https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md |
Italian Society Of Medical And Interventional Radiology (SIRM) COVID-19 Database | COVID-19 | X-ray and CT | 68 | https://www.sirm.org/category/senza-categoria/covid-19/ | |
LDOCTCXR | Pneumonia and normal | X-ray | [93] | 3883 pneumonia and 1349 normal | https://data.mendeley.com/datasets/rscbjbr9sj/3 |
Lung image database consortium (LIDC) | Lung Cancer | CT | [149] | 1018 | https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI |
Sajid’s Kaggle dataset | COVID-19 and normal | X-ray | 28 normal, 70 COVID-19 | https://www.kaggle.com/nabeelsajid917/covid-19-x-ray-10000-images | |
Mooney’s Kaggle dataset | Pneumonia and normal | X-ray | 5863 | https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia | |
COVID-CT Dataset | COVID-19 and normal | CT | 349 COVID-19 and 463 non-COVID-19 | https://github.com/UCSD-AI4H/COVID-CT | |
Mendeley Augmented COVID-19 X-ray Images Dataset | COVID-19 and normal | X-ray | 912 | https://data.mendeley.com/datasets/2fxz4px6d8/4 | |
COVID-19 Chest X-Ray Dataset Initiative | COVID-19 | X-ray | 55 | https://github.com/agchung/Figure1-COVID-chestxray-dataset |
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Kieu, S.T.H.; Bade, A.; Hijazi, M.H.A.; Kolivand, H. A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions. J. Imaging 2020, 6, 131. https://doi.org/10.3390/jimaging6120131
Kieu STH, Bade A, Hijazi MHA, Kolivand H. A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions. Journal of Imaging. 2020; 6(12):131. https://doi.org/10.3390/jimaging6120131
Chicago/Turabian StyleKieu, Stefanus Tao Hwa, Abdullah Bade, Mohd Hanafi Ahmad Hijazi, and Hoshang Kolivand. 2020. "A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions" Journal of Imaging 6, no. 12: 131. https://doi.org/10.3390/jimaging6120131