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

Chun-Fu Yeh, Hsien-Tzu Cheng, Andy Wei, Keng-Chi Liu, Mong-Chi Ko, Po-Chen Kuo, Ray-Jade Chen, Po-Chang Lee, Jen-Hsiang Chuang, Chi-Mai Chen, Nai-Kuan Chou, Yeun-Chung Chang, Kuan-Hua Chao, Yi-Chin Tu, Tyng-Luh Liu

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.

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