A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening

Yeh, Chun-Fu; Cheng, Hsien-Tzu; Wei, Andy; Chen, Hsin-Ming; Kuo, Po-Chen; Liu, Keng-Chi; Ko, Mong-Chi; Chen, Ray-Jade; Lee, Po-Chang; Chuang, Jen-Hsiang; Chen, Chi-Mai; Chen, Yi-Chang; Lee, Wen-Jeng; Chien, Ning; Chen, Jo-Yu; Huang, Yu-Sen; Chang, Yu-Chien; Huang, Yu-Cheng; Chou, Nai-Kuan; Chao, Kuan-Hua; Tu, Yi-Chin; Chang, Yeun-Chung; Liu, Tyng-Luh

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.