ArticleResults in physics2021
COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images.
Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.Computers in biology and medicine · 2023Article
- COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- A novel framework based on deep learning for COVID-19 diagnosis from X-ray images.PeerJ. Computer science · 2023Article
- Classification of Alzheimer's Disease Based on Weakly Supervised Learning and Attention Mechanism.Brain sciences · 2022Article
- A novel deep fusion strategy for COVID-19 prediction using multimodality approach.Computers & electrical engineering : an international journal · 2022Article
- TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images.Scientific reports · 2022Article
- Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray.Sensors (Basel, Switzerland) · 2022Article
- CrodenseNet: An efficient parallel cross DenseNet for COVID-19 infection detection.Biomedical signal processing and control · 2022Article
- Effective hybrid deep learning model for COVID-19 patterns identification using CT images.Expert systems · 2022Article
- Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images.Biomedical signal processing and control · 2022Article
- QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds.Diagnostics (Basel, Switzerland) · 2022Article
- Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients.Biomedical signal processing and control · 2022Article
- A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation.Journal of personalized medicine · 2022Article
- Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.Computational intelligence and neuroscience · 2022Article
- Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.Journal of healthcare engineering · 2022Article
- Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.Journal of healthcare engineering · 2022Article
- Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.Computational intelligence and neuroscience · 2022Article
- COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.Biocybernetics and biomedical engineeringArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.
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