COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images
Source abstract
Abstract
Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images, respectively. Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused. Parallel architecture, which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people, was considered. The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1-score of 99.93%, MSE of 0.021%, and RMSE of 0.016% in a large-scale dataset. This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
Show source-provided references
1. Early prediction of the 2019 novel coronavirus outbreak in the mainland China based on simple mathematical model10.1109/ACCESS.2020.2979599 ↗
2. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study10.1016/S0140-6736(20)30211-7 ↗
3. Coronavirus disease (COVID-19): A primer for emergency physicians
4. The impact of the COVID-19 epidemic on the utilization of emergency dental services10.1016/j.jds.2020.02.002 ↗
5. Worldmeter, [Online]. Available: https://www.worldometers.info/coronavirus/worldwide-graphs/ [Accessed: 6 June 2020].
6. Detection of SARS-CoV-2 in different types of clinical specimens
7. Imaging profile of the COVID-19 infection: Radiologic findings and literature review
8. Error and discrepancy in radiology: Inevitable or avoidable?10.1007/s13244-016-0534-1 ↗
9. Scalable and accurate deep learning with electronic health records10.1038/s41746-018-0029-1 ↗
10. A review of fog computing and machine learning: Concepts, applications, challenges, and open issues10.1109/ACCESS.2019.2947542 ↗
11. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
12. Skin cancer classification using deep learning and transfer learning
13. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques10.1007/s00521-018-3882-6 ↗
14. Trainable model for segmenting and identifying nasopharyngeal carcinoma10.1016/j.compeleceng.2018.07.044 ↗
15. Convolutional neural networks for multi-class brain disease detection using mri images10.1016/j.compmedimag.2019.101673 ↗
16. Detecting lumbar implant and diagnosing scoliosis from Vietnamese X-ray imaging using the pre-trained API models and transfer learning10.32604/cmc.2020.013125 ↗
17. P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta et al., “Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning,” arXiv Prepr. arXiv: 1711.05225, pp. 3–9, 2017 (Pre-print).
18. Clinically applicable deep learning for diagnosis and referral in retinal disease10.1038/s41591-018-0107-6 ↗
19. Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images
20. J. P. Cohen, P. Morrison and L. Dao, “COVID-19 image data collection,” 2020. [Online]. Available: https://github.com/ieee8023/covid-chestxray-dataset.
21. Kaggle’s Chest X-ray Images (Pneumonia) dataset 2020. [Online]. Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.
22. E. E. 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 Prepr. arXiv: 2003.11055, 2020 (Pre-print).
23. A. Narin, C. Kaya and Z. Pamuk, “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks,” arXiv Prepr. arXiv: 2003.10849, 2020 (Pre-print).10.1007/s10044-021-00984-y ↗
24. Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and topsis methods10.1109/ACCESS.2020.2995597 ↗
25. S. H. Kassani, P. H. Kassasni, M. J. Wesolowski, K. A. Schneider and R. Deters, “Automatic detection of coronavirus disease (COVID-19) in X-ray and ct images: A machine learning-based approach,” arXiv Prepr. arXiv: 2004.10641, pp. 1–18, 2020 (Pre-print).
26. 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 Prepr. arXiv: 2003.12338, 2020 (Pre-print).
27. Global forecasting confirmed and fatal cases of COVID-19 outbreak using autoregressive integrated moving average model10.3389/fpubh.2020.580327 ↗
28. IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification10.1007/s13042-020-01248-7 ↗
29. P. Kumar and S. Kumari, “Detection of coronavirus disease (COVID-19) based on deep features,” Preprint, 2020030300, 2020 (Pre-print).
30. O. Gozes, M. Frid-Adar, H. Greenspan, P. D. Browning, H. Zhang et al., “Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis,” arXiv Prepr. arXiv: 2003.05037, pp. 1–19, 2020 (Pre-print).
31. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study
32. Radiopaedia dataset, 2020. [Online]. Available: https://radiopaedia.org/search?lang=us&q=covid&scope=cases#collapse-by-diagnostic-certainties [Accessed: 11 May 2020].
33. Italian Society of Medical and Interventional Radiology (SIRM), 2020. [Online]. Available: https://www.sirm.org/en/italian-society-of-medical-and-interventional-radiology/ [Accessed: 11 May 2020].
34. Radiological Society of North America (RSNA), 2020. [Online]. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [Accessed: 11 May 2020].
35. Kaggle’s Chest X-ray Images (Pneumonia) dataset, 2020. [Online]. Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia%20%20%20 [Accessed: 12 May 2020].
36. Image enhancement on digital X-ray images using N-clahe
37. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
38. A multimodal deep learning framework using local feature representations for face recognition10.1007/s00138-017-0870-2 ↗
39. A multimodal biometric system for personal identification based on deep learning approaches
40. Unsupervised feature learning for audio classification using convolutional deep belief networks
41. Al-Waisy · Deep Learning in Computer Vision: Principles and Applications · 2020 · 8910.1201/9781351003827-4 ↗
42. Training products of experts by minimizing contrastive divergence10.1162/089976602760128018 ↗
43. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
44. Jain · Handbook of Biometrics · 2007 · 1
45. E. J. S. Luz, P. L. Silva, R. Silva, L. Silva, G. Moreira et al., “Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images,” arXiv Prepr. arXiv: 2004.05717, pp. 1–10, 2020 (Pre-print).