ArticleJournal of clinical medicine2022
A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images.
Article in Journal of clinical medicine, 2022. 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.
- Charting the artificial intelligence revolution in orthodontics : A bibliometric analysis of key players, collaborations, and research directions.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Review
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- Advances in Musculoskeletal Imaging and Their Applications.Journal of clinical medicine · 2023Article
- Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.Polish journal of radiology · 2023Article
- An improvement of the CNN-XGboost model for pneumonia disease classification.Polish journal of radiology · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundThis paper presents a novel lightweight approach based on machine learning methods supporting COVID-19 diagnostics based on X-ray images. The presented schema offers effective and quick diagnosis of COVID-19.
methodsReal data (X-ray images) from hospital patients were used in this study. All labels, namely those that were COVID-19 positive and negative, were confirmed by a PCR test. Feature extraction was performed using a convolutional neural network, and the subsequent classification of samples used Random Forest, XGBoost, LightGBM and CatBoost.
resultsThe LightGBM model was the most effective in classifying patients on the basis of features extracted from X-ray images, with an accuracy of 1.00, a precision of 1.00, a recall of 1.00 and an F1-score of 1.00.
conclusionThe proposed schema can potentially be used as a support for radiologists to improve the diagnostic process. The presented approach is efficient and fast. Moreover, it is not excessively complex computationally.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.