A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
Abstract
We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.
Cite
@article{arxiv.2004.12786,
title = {A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening},
author = {Chun-Fu Yeh and Hsien-Tzu Cheng and Andy Wei and Hsin-Ming Chen and Po-Chen Kuo and Keng-Chi Liu and Mong-Chi Ko and Ray-Jade Chen and Po-Chang Lee and Jen-Hsiang Chuang and Chi-Mai Chen and Yi-Chang Chen and Wen-Jeng Lee and Ning Chien and Jo-Yu Chen and Yu-Sen Huang and Yu-Chien Chang and Yu-Cheng Huang and Nai-Kuan Chou and Kuan-Hua Chao and Yi-Chin Tu and Yeun-Chung Chang and Tyng-Luh Liu},
journal= {arXiv preprint arXiv:2004.12786},
year = {2020}
}
Comments
14 pages, 6 figures