PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320706 - e0320706

Published: April 14, 2025

Despite tremendous efforts devoted to the area, image texture analysis is still an open research field. This paper presents algorithm and experimental results demonstrating feasibility of developing automated tools detect abnormal X-ray images based on tissue attenuation. Specifically, this work proposes using variability characterised by singular values conditional indices extracted from value decomposition (SVD) as features. In addition, introduces a “tuning weight" parameter consider attenuation in tissues affected pathologies. weight estimated coefficient variation minimum covariance determinant bandwidth yielded non-parametric distribution variance-decomposition proportions SVD. When multiplied two features (singular indices), single acts tuning weight, reducing misclassification improving classic performance metrics, such true positive rate, false negative predictive values, discovery area-under-curve, accuracy total cost. The proposed method implements ensemble bagged trees classification model classify chest COVID-19, viral pneumonia, lung opacity, or normal. It was tested challenging, imbalanced public dataset. show 88% without applying 99% with its application. outperforms state-of-the-art methods, attested all metrics.

Language: Английский