Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model
Source abstract
Abstract
COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Digital Imaging for Blood Diseases · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. “Coronavirus disease 2019 (COVID-19) situation report–94,” World Health Organization. 2019. URL: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200423-sitrep-94-COVID-19.pdf [accessed 2021-02-04].
2. Artificial neural network-based deep learning model for covid-19 patient detection using x-ray chest images10.1155/2021/5513679 ↗
3. A classification–detection approach of COVID-19 based on chest X-ray and CT by using Keras pre-trained deep learning models10.32604/cmes.2020.011920 ↗
4. Classification of COVID-19 from chest x-ray images using deep convolutional neural network
5. W. Zhang, B. Pogorelsky, M. Loveland and T. Wolf, “Classification of covid-19 x-ray images using a combination of deep and handcrafted features,” Accessed: Oct. 16, 2021. [Online]. Available: https://arxiv.org/abs/2101.07866v1.
6. Classification of COVID-19 in chest x-ray images using DeTraC deep convolutional neural network
7. Transfer learning to detect covid-19 automatically from x-ray images using convolutional neural networks10.1155/2021/8828404 ↗
8. A deep neural network to distinguish COVID-19 from other chest diseases using x-ray images
9. COVID-19 classification in x-ray chest images using a new convolutional neural network: CNN-COVID
10. Balas · Smart Innovation, Systems and Technologies · 201910.1007/978-3-030-11479-4 ↗
11. Classification of x-ray images for detecting covid-19 using deep transfer learning
12. Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms
13. Deep learning-based multi-class COVID-19 classification with x-ray images
14. An efficient cnn model for covid-19 disease detection based on x-ray image classification10.1155/2021/6621607 ↗
15. Detection of covid-19 from chest x-ray images using convolutional neural networks10.1177/2472630320958376 ↗
16. Automated detection of COVID-19 cases using deep neural networks with X-ray images
17. An accuracy vs. complexity comparison of deep learning architectures for the detection of COVID-19 disease
18. COVID-19 detection on chest x-ray and ct scan images using multi-image augmented deep learning model
19. Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images
20. AI in medical imaging informatics: Current challenges and future directions10.1109/JBHI.2020.2991043 ↗
21. Randomly initialized convolutional neural network for the recognition of covid-19 using x-ray images
22. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
23. Prediction of COVID-19-pneumonia based on selected deep features and one class kernel extreme learning machine10.1016/j.compeleceng.2020.106960 ↗
24. Time series forecasting of COVID-19 transmission in Asia pacific countries using deep neural networks10.1007/s00779-020-01494-0 ↗
25. Classification of positive COVID-19 CT scans using deep learning10.32604/cmc.2021.013191 ↗
26. Classification of COVID-19 CT scans via extreme learning machine10.32604/cmc.2021.015541 ↗
27. P. Mooney, “Chest x-ray images (pneumonia),” (March, 2020). [Online]. Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/metadata.
28. T. Rahman, “COVID-19 chest X-ray database,” (February, 2021). [Online]. Available: https://www.kaggle.com/tawsifurrahman/COVID19-radiography-database.
29. A. Haghanifar, M. Molahasani Majdabadi and S. Ko, “COVID-19 chest X-ray image repository,” figshare, 28-Jun-2020. [Online]. Available: https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328.
30. An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine10.1016/j.mehy.2019.109433 ↗
31. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally et al., “SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0.5 MB model size,” Feb. 2016, Accessed: Oct. 16, 2021. [Online]. Available: http://arxiv.org/abs/1602.07360.
32. Fast, Embedded Implementation of a Convolutional Neural Network for Image Recognition10.13140/RG.2.1.1778.9681 ↗
33. A. Saad, “Classification of COVID-19, normal and pneumonia based on enhancement X-ray image and squeeze Net model,” figshare. Dataset. https://doi.org/10.6084/m9.figshare.16408392.v1.
34. Improving the sharpness of digital image using an amended unsharp mask filter10.5815/ijigsp.2019.03.01 ↗
35. Application of histogram equalization for image enhancement in corrosion areas10.1155/2021/8883571 ↗
36. Image thresholding using the complement feature10.3844/ajeassp.2020.311.317 ↗
37. Transfer learning for image classification using hebbian plasticity principles
38. A survey of transfer learning10.1186/s40537-016-0043-6 ↗
39. A survey on transfer learning10.1109/TKDE.2009.191 ↗
40. Transfer learning strategies for deep learning-based PHM algorithms10.3390/app10072361 ↗
41. The relationship between precision-recall and ROC curves
42. Evaluation: From precision, recall and F-measure to ROC, informedness