Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques
Source abstract
Abstract
Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task. We used a publicly open CXR image dataset and implemented the detection model with task-specific pre-processing and near 80:20 split. This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597, which shall help better decision-making for various aspects of identification and treat the infection.
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 (30)
Show source-provided references
1. Use of chest imaging in COVID-19: A rapid advice guide, 11 June 2020
2. Emerging 2019 novel coronavirus (2019-nCoV) pneumonia10.1148/radiol.2020200274 ↗
3. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China10.1001/jama.2020.1585 ↗
4. Chest X-ray findings and visual quantitative assessment of COVID-19 pneumonia
5. Review of the chest CT differential diagnosis of ground-glass opacities in the COVID era10.1148/radiol.2020202504 ↗
6. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)
7. Covid-net: A tailored deep convolutional neural network design for detection of Covid-19 cases from chest X-ray images
8. E. Hemdan, M. Shouman and M. Karar, “Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in X-ray images,” arXiv preprint arXiv: 2003.11055, vol. 20, no. 2, pp. 1–14, 2003.
9. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey10.1016/j.scs.2020.102589 ↗
10. Efficiency of artificial intelligence in detecting COVID-19 pneumonia and other pneumonia causes by quantum fourier transform method
11. Deep learning approaches for COVID-19 detection based on chest X-ray images10.1016/j.eswa.2020.114054 ↗
12. Explainable COVID-19 detection using chest CT scans and deep learning10.3390/s21020455 ↗
13. CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection10.1016/j.asoc.2020.106912 ↗
14. COVID-19 detection in chest X-ray images using a deep learning approach
15. C. Fu, W. Liu, A. Ranga, A. Tyagi and A. Berg, “DSSD: Deconvolutional single shot detector,” arXiv preprint arXiv: 1701.06659, vol. 13, no. 4, pp. 12–23, 2017.
16. SSD: Single shot multibox detector
17. Deep residual learning for image recognition
18. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement10.1023/B:VLSI.0000028532.53893.82 ↗
19. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
20. A unified multi-scale deep convolutional neural network for fast object detection
21. Object detection via a multi-region and semantic segmentation-aware cnn model
22. A novel approach to perform analysis and prediction on breast cancer dataset using R10.14257/ijgdc.2018.11.2.05 ↗
23. Reconet: Multi-level pre-processing of chest X-rays for covid-19 detection using convolutional neural networks
24. R. Kalkreuth and P. Kaufmann, “COVID-19: A survey on public medical imaging data resources,” arXiv preprint arXiv: 2004.04569, vol. 11, no. 2, pp. 1–4, 2004.
25. Covid prediction from X-ray images
26. P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. Plataniotis et al., “Covid-caps: A capsule network-based framework for identification of covid-19 cases from X-ray images,” arXiv preprint arXiv: 2004.02696, vol. 12, no. 3, pp. 1–6, 2004.
27. S. Rajpal, N. Kumar and A. Rajpal, “Cov-elm classifier: An extreme learning machine based identification of COVID-19 using chest X-ray images,” arXiv preprint arXiv: 2007.08637, vol. 22, no. 3, pp. 1–8, 2007.
28. B. Cruz, J. Sölter, M. Bossa and A. Husch, “On the composition and limitations of publicly available COVID-19 X-ray imaging datasets,” arXiv preprint arXiv: 2008.11572, vol. 23, no. 2, pp. 1–9, 2008.
29. A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics
30. L. Cui, R. Ma, P. Lv, X. Jiang, Z. Gao et al., “MDSSD: Multi-scale deconvolutional single shot detector for small objects,” arXiv preprint arXiv: 1805.07009, vol. 14, no. 2, pp. 1–4, 2018.