1. Analysis of generalizability on predicting COVID-19 from chest X-ray images using pre-trained deep models.
- Author
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de Sousa Freire, Natalia, de Souza Leo, Pedro Paulo, Tiago, Leonardo Albuquerque, de Almeida Campos Gonalves, Alberto, Pinto, Rafael Albuquerque, dos Santos, Eulanda Miranda, and Souto, Eduardo
- Subjects
X-rays ,X-ray imaging ,MACHINE learning ,TRANSFORMER models ,COVID-19 ,IMAGE intensifiers - Abstract
Machine learning methods have been extensively employed to predict COVID-19 using chest X-ray images in numerous studies. However, a machine learning model must exhibit robustness and provide reliable predictions for diverse populations, beyond those used in its training data, to be truly valuable. Unfortunately, the assessment of model generalisability is frequently overlooked in current literature. In this study, we investigate the generalisability of three classification models – ResNet50v2, MobileNetv2, and Swin Transformer – for predicting COVID-19 using chest X-ray images. We adopt three concurrent approaches for evaluation: the internal-and-external validation procedure, lung region cropping, and image enhancement. The results show that the combined approaches allow deep models to achieve similar internal and external generalisation capability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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