Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
Open accessAbstract
Objective This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.Method CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.Results The CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).Conclusion This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.
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CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images.
“CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images [256–260].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [256]
The Introduction reports that researchers have applied CNN models to detect and classify COVID-19 using chest X-ray images. It does not say CNNs are the primary diagnostic tool in hospitals worldwide; the Conclusion instead calls for further validation of the models. (LLM, conf=0.86)
Experimental — accuracy not yet measuredImage pre-processing using contrast-to-noise ratio, removing irrelevant regions etc. improved the DNN performance.
“The image pre-processing using contrast-to-noise ratio, removing irrelevant regions etc. improved the DNN performance [267].”
From Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks (2023) · cites this paper as [267]
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1 retracted
RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia
Butt, C., Gill, J., Chun, D., & Babu, B. A. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01714-3 · Cited by 333 · see citing works
RetractedAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415 · see citing works
Self-citationInterrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
Logistic Regression in Rare Events Data
King, G., & Zeng, L. (2001) · Political Analysis · doi:10.1093/oxfordjournals.pan.a004868 · Cited by 4,316 · see citing works
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,151 · see citing works
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,607 · see citing works
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L., & Wang, J. (2017) · arXiv · doi:10.48550/arxiv.1712.04621 · Cited by 2,499 · see citing works
Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338 · see citing works
Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054 · see citing works
Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913 · see citing works
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,668 · see citing works
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
Khan, A. I., Shah, J. L., & Bhat, M. M. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105581 · Cited by 1,312 · see citing works
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray ImagesPreprint
Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 1,181 · see citing works
A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)Preprint
Wang, S., Kang, B., Ma, J., et al. (2020) · medRxiv · doi:10.1101/2020.02.14.20023028 · Cited by 1,035 · see citing works
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 960 · see citing works
Detection of Coronavirus Disease (COVID-19) Based on Deep FeaturesPreprint
Sethy, P. K., & Behera, S. K. (2020) · Preprints.org · doi:10.20944/preprints202003.0300.v1 · Cited by 723 · see citing works
COVID-19 Image Data CollectionPreprint
Cohen, J., Morrison, P., & Dao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11597 · Cited by 722 · see citing works
Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695 · see citing works
Lung Infection Quantification of COVID-19 in CT Images with Deep LearningPreprint
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D. G., & Shi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04655 · Cited by 489 · see citing works
Kassem, M. A. A., Hosny, K. M., & Fouad, M. M. (2020) · IEEE Access · doi:10.1109/access.2020.3003890 · Cited by 424 · see citing works
New machine learning method for image-based diagnosis of COVID-19
Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235187 · Cited by 400 · see citing works
CT Imaging and Differential Diagnosis of COVID-19
Dai, W.-C., Zhang, H., Yu, J., Xu, H.-J., Chen, H., Luo, S., Zhang, H., Liang, L.-H., Wu, X.-L., Yi, L., et al. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537120913033 · Cited by 358 · see citing works
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT imagesPreprint
Song, Y., Zheng, S., Li, L., et al. (2020) · medRxiv · doi:10.1101/2020.02.23.20026930 · Cited by 332 · see citing works
A review of deep learning in image recognition
Pak, M., & Kim, S.-H. (2017) · International Conference on Computer Applications and Information Processing Technology (CAIPT) · doi:10.1109/caipt.2017.8320684 · Cited by 252 · see citing works
Wang, L., & Wong, A. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09871 · Cited by 248 · see citing works
Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images
Kermany, D. (2018) · Mendeley · doi:10.17632/rscbjbr9sj.3 · Cited by 172 · see citing works
Gong, J., Liu, J., Wen, H., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06533-w · Cited by 113 · see citing works
Heidari, M., Khuzani, A. Z., Hollingsworth, A. B., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaa1ca · Cited by 91 · see citing works
Self-citationZhao, X., Qi, S., Zhang, B., Ma, H., Qian, W., Yao, Y., & Sun, J. (2019) · Journal of X-Ray Science and Technology · doi:10.3233/xst-180490 · Cited by 63 · see citing works
Heidari, M., Mirniaharikandehei, S., Liu, W., Hollingsworth, A. B., Liu, H., & Zheng, B. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2946490 · Cited by 44 · see citing works
Self-citationLei, P., Huang, Z., Liu, G., Wang, P., Song, W., Mao, J., Shen, G., Zhou, S., Qian, W., & Jiao, J. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200670 · Cited by 39 · see citing works
Wang, K., Patel, B., Wang, L., Wu, T., Zheng, B., & Li, J. (2019) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2019.1628133 · Cited by 16 · see citing works
Self-citationLiu, W., Zargaria, A., Thai, T., et al. (2019) · doi:10.1117/12.2510037 · Cited by 4 · see citing works
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Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044 · see citing works
Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283 · see citing works
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210 · see citing works
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168 · see citing works
M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133 · see citing works
A systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87 · see citing works
- Authors
- Heidari, M., Mirniaharikandehei, S., Khuzani, A. Z., Danala, G., Qiu, Y., & Zheng, B.
- Affiliations
- University of Oklahoma (US)
- University of California, Santa Cruz (US)
- Corresponding author
- Heidari, Morteza
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 144
- Pages
- 104284
- ISSN
- 1386-5056 (ISSN-L)
- PMC
- PMC7510591
- OpenAlex
- W3036908895
- Cited by
- 423
- References
- 39
- Citations by year
- 2026: 14 · 2025: 27 · 2024: 70 · 2023: 106 · 2022: 109 · 2021: 85 · 2020: 11 · 2012: 1
- FWCI
- 37.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Cancer Institute
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 pandemic, pneumonia detection, chest X-ray images, computer-aided diagnosis, image preprocessing, histogram equalization, pseudo-color images, transfer learning, convolutional neural networks
- MeSH terms
- Deep Learning, COVID-19, SARS-CoV-2, Algorithms, Diagnosis, Computer-Assisted, Humans, Radiography, Thoracic, Tomography, X-Ray Computed, Neural Networks, Computer
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