ArticleComputers in biology and medicine2021
LBP-based information assisted intelligent system for COVID-19 identification.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 27 citations in OpenAlex.
- COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms.BMC bioinformatics · 2024Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.Diagnostics (Basel, Switzerland) · 2023Article
- Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features.Frontiers in human neuroscience · 2023Article
- A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.Journal of grid computing · 2022Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
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Abstract
A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.
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