Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

Frontiers in Medicine

article

Seung Hoon Yoo¹  Nguen Viet Nhung²  Byung Jun Min³  Ho Lee⁴  Hui Geng¹  Tin Lok Chiu¹  Siu Ki Yu¹  Dae Chul Cho⁵  Jin Heo⁵  Min Sung Choi⁵  Il Hyun Choi⁵  Cong Cung Van² 

[1] Medical Physics and Research Department, Hong Kong Sanatorium & Hospital;Vietnam National Lung Hospital;Department of Radiation Oncology, Chungbuk National University Hospital;Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine;Artificial Intelligent Research Lab

关键词: chest X-ray radiography;    COVID-19;    deep learning;    image classification;    neural network;    tuberculosis;   

DOI  :  10.3389/fmed.2020.00427

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.

【 授权许可】

CC BY