ArticleBiomedical signal processing and control2022
CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.Brain informatics · 2023Article
- IRCM-Caps: An X-ray image detection method for COVID-19.The clinical respiratory journal · 2023Article
- Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis.Multimedia tools and applications · 2023Article
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.Biomedical signal processing and control · 2023Article
- Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.New generation computing · 2023Article
- COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images.Sensors (Basel, Switzerland) · 2022Article
- Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images.SN computer science · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score.
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