Classification of COVID-19 CT Scans via Extreme Learning Machine
Source abstract
Abstract
Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors; this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an Extreme Learning Machine (ELM) served to assess GA fitness. Based on the ELM losses, the most discriminatory features were selected and saved as an ELM Model. Test images were sent to the model, and the best-selected features compared to those of the trained model to allow final predictions. Validation employed the collected chest CT scans. The best predictive accuracy of the ELM classifier was 93.9%; the scheme was effective.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Sparse and Compressive Sensing Techniques · SARS-CoV-2 detection and testing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
Show source-provided references
1. A novel coronavirus from patients with pneumonia in China10.1056/NEJMoa2001017 ↗
2. A. E. Gorbalenya, S. C. Baker, R. Baric, R. J. d. Groot, C. Drosten et al., “Severe acute respiratory syndrome-related coronavirus: The species and its viruses-a statement of the Coronavirus Study Group,” 2020.10.1101/2020.02.07.937862 ↗
3. Real-time forecasts of the COVID-19 epidemic in China from february 5th to february 24th10.1016/j.idm.2020.02.002 ↗
4. World health organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)10.1016/j.ijsu.2020.02.034 ↗
5. First cases of coronavirus disease 2019 (COVID-19) in France: Surveillance, investigations and control measures, January 2020
6. Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan10.1002/jmv.25689 ↗
7. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays10.1016/j.cmpb.2020.105608 ↗
8. Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm
9. Covid-19 control by computer vision approaches: A survey10.1109/ACCESS.2020.3027685 ↗
10. InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray
11. StomachNet: Optimal deep learning features fusion for stomach abnormalities classification10.1109/ACCESS.2020.3034217 ↗
12. Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection10.1016/j.patrec.2019.11.014 ↗
13. Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists10.3390/diagnostics10080565 ↗
14. Deep convolutional neural networks for WCE abnormality detection: CNN architecture, region proposal and transfer learning10.1109/ACCESS.2019.2901568 ↗
15. Texture based medical image classification by using multi-scale gabor rotation-invariant local binary pattern (MGRLBP)10.1007/s10586-017-1269-6 ↗
16. Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images
17. Fusing fine-tuned deep features for skin lesion classification10.1016/j.compmedimag.2018.10.007 ↗
18. Gastrointestinal diseases segmentation and classification based on duo-deep architectures10.1016/j.patrec.2019.12.024 ↗
19. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images10.1016/j.patrec.2019.11.019 ↗
20. An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification10.1186/s12885-018-4465-8 ↗
21. Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection10.1002/jemt.23447 ↗
22. Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection
23. Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks10.1007/s13246-020-00865-4 ↗
24. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang et al., “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv: 1704.04861, 2017.
25. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT10.1148/radiol.2020200905 ↗
26. F. Shan, Y. Gao, J. Wang, W. Shi, N. Shi et al., “Lung infection quantification of covid-19 in ct images with deep learning,” arXiv preprint arXiv: 2003.04655, 2020.
27. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays10.3892/etm.2020.8797 ↗
28. 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 ↗
29. 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.
30. V. Rajinikanth, S. Kadry, K. P. Thanaraj, K. Kamalanand and S. Seo, “Firefly-algorithm aupported scheme to detect COVID-19 lesion in lung CT scan images using shannon entropy and markov-random-field,” arXiv preprint arXiv: 2004.09239, 2020.
31. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)
32. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning
33. A new COVID-19 patients detection strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier10.1016/j.knosys.2020.106270 ↗
34. Detecting COVID-19 patients based on fuzzy inference engine and deep neural network10.1016/j.asoc.2020.106906 ↗
35. COVID-19 detection through transfer learning using multimodal imaging data10.1109/ACCESS.2020.3016780 ↗
36. CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection10.1016/j.asoc.2020.106912 ↗
37. Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction10.1002/jemt.23429 ↗
38. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns10.1109/TPAMI.2002.1017623 ↗
39. Automated ambient recognition method based on dynamic center mirror local binary pattern: DCMLBP10.1016/j.apacoust.2019.107165 ↗
40. An efficient algorithm for fractal analysis of textures
41. Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests10.1016/j.neucom.2015.11.034 ↗
42. A new method of feature fusion and its application in image recognition10.1016/j.patcog.2004.12.013 ↗
43. Multilayer one-class extreme learning machine10.1016/j.neunet.2019.03.004 ↗