1. A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images.
- Author
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Giełczyk, Agata, Marciniak, Anna, Tarczewska, Martyna, Kloska, Sylwester Michal, Harmoza, Alicja, Serafin, Zbigniew, and Woźniak, Marcin
- Subjects
X-ray imaging ,CONVOLUTIONAL neural networks ,COVID-19 ,FEATURE extraction ,DIAGNOSTIC use of polymerase chain reaction - Abstract
Background: This 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. Methods: Real 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. Results: The 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. Conclusion: The 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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