Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 259, P. 108518 - 108518

Published: Nov. 25, 2024

Language: Английский

Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification

Artificial Intelligence in Medicine, Journal Year: 2025, Volume and Issue: 165, P. 103135 - 103135

Published: April 23, 2025

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails include these critical pathological areas in the generated masks. To address this limitation, our study, we tackled challenge of precise segmentation and mask generation by developing novel approach, using CycleGAN, encompasses affected pathologies within region interest, allowing extraction relevant radiomic features linked pathologies. Furthermore, adopted feature selection approach focus analysis on most significant features. The results proposed pipeline are promising, with an average accuracy 92.05% AUC 89.48% multi-label classification effusion infiltration acquired from ChestX-ray14 dataset, XGBoost model. applying methodology 14 diseases dataset resulted 83.12%, outperforming previous studies. This research highlights importance effective accurate diseases. promising underscore its potential broader applications

Language: Английский

Report-Concept Textual-Prompt Learning for Enhancing X-ray Diagnosis

Published: Oct. 26, 2024

Language: Английский