Coronavirus Detection and Analysis on Chest CT with Deep Learning

Abstract

The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.

References in corpus (1)

• Very Deep Convolutional Networks for Large-Scale Image Recognition

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• Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning
• Intra-model Variability in COVID-19 Classification Using Chest X-ray Images
• Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
• Classification of pediatric pneumonia using chest X-rays by functional regression
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging