Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder
Source abstract
Abstract
The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Anomaly Detection Techniques and Applications · Digital Imaging for Blood Diseases
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
Show source-provided references
1. Sensitivity of chest ct for covid-19: Comparison to RT-PCR10.1148/radiol.2020200432 ↗
2. Chest CT accuracy in diagnosing covid-19 during the peak of the italian epidemic: A retrospective correlation with RT-PCR testing and analysis of discordant cases10.1016/j.ejrad.2020.109192 ↗
3. Can chest ct improve sensitivity of covid-19 diagnosis in comparison to pcr? A meta-analysis10.1186/s43163-020-00039-9 ↗
4. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V210.1016/j.imu.2020.100360 ↗
5. COVID-2019 detection using X-ray images and artificial intelligence hybrid systems
6. Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks10.1007/s13246-020-00865-4 ↗
7. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images10.1016/j.cmpb.2020.105581 ↗
8. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine
9. Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning10.3390/sym12040651 ↗
10. OVID-MobileXpert: On-device COVID-19 patient triage and follow-up using chest X-rays
11. Using X-ray images and deep learning for automated detection of coronavirus disease
12. U-net: Convolutional networks for biomedical image segmentation
13. Tuan · Handbook of Data Science Approaches for Biomedical Engineering · 2020 · 25110.1016/B978-0-12-818318-2.00009-X ↗
14. An Efficient brain MR images segmentation hardware using kernel fuzzy C-means
15. A. Shoeibi, M. Khodatars, R. Alizadehsani, N. Ghassemi, M. Jafari et al., “Automated detection and forecasting of covid-19 using deep learning techniques: A review,” arXiv preprint arXiv: 2007.10785, 2020.
16. COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-Ray images10.1109/JBHI.2020.3037127 ↗
17. E-DiCoNet: Extreme learning machine-based classifier for diagnosis of COVID-19 using deep convolutional network
18. Accuracy of conventional and machine learning enhanced chest radiography for the assessment of COVID-19 pneumonia: Intra-individual comparison with CT10.3390/jcm9113576 ↗
19. Computer-aided detection (CAD) for COVID-19 based on chest X-Ray images using convolutional neural network10.1088/1757-899X/982/1/012004 ↗
20. Pictures of X-rays displayed in monitors for deep learning-based COVID-19 screening: Implications for mobile application development
21. M. Farooq and A. Hafeez, “Covid-resnet: A deep learning framework for screening of covid19 from radiographs,” arXiv preprint arXiv: 2003.14395, 2020.
22. Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms
23. E. E.-D. Hemdan, M. A. Shouman and M. E. Karar, “Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images,” arXiv preprint arXiv: 2003.11055, 2020.
24. J. Zhang, Y. Xie, Y. Li, C. Shen and Y. Xia, “Covid-19 screening on chest x-ray images using deep learning-based anomaly detection,” arXiv preprint arXiv: 2003.12338, 2020.
25. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images
26. A novel transfer learning-based approach for pneumonia detection in chest X-ray images10.3390/app10020559 ↗
27. A. Narin, C. Kaya and Z. Pamuk, “Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks,” arXiv preprint arXiv: 2003.10849, 2020.10.1007/s10044-021-00984-y ↗
28. Extracting and composing robust features with denoising autoencoders
29. Image denoising and inpainting with deep neural networks
30. Rényi divergence and Kullback–Leibler divergence10.1109/TIT.2014.2320500 ↗
31. Why does unsupervised pre-training help deep discriminant learning
32. Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates10.1016/j.jco.2019.101438 ↗
33. Imagenet classification with deep convolutional neural networks
34. Reducing the dimensionality of data with neural networks10.1126/science.1127647 ↗
35. S. Rifai, X. Glorot, Y. Bengio and P. Vincent, “Adding noise to the input of a model trained with a regularized objective,” arXiv preprint arXiv: 1104.3250, 2011.
36. SMOTE: Synthetic minority over-sampling technique10.1613/jair.953 ↗
37. ADASYN: Adaptive synthetic sampling approach for imbalanced learning
38. Identifying medical diagnoses and treatable diseases by image-based deep learning10.1016/j.cell.2018.02.010 ↗
39. Automated detection of covid-19 cases using deep neural networks with x-ray images10.1016/j.compbiomed.2020.103792 ↗