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Study on the TB and Non-TB Diagnosis Using Two-Step Deep Learning-Based Binary Classifier

Published

Journal of Instrumentation, 2020, 15.10: P10011

Authors

Seung Hoon Yoo1, GengHui Geng1, Tin Lok Chiu1, Siu Ki Yu1, Dae Chul Cho2, Jin Heo2, Min Sung Choi2, Il Hyun Cho2, Cong Cung Van3, Nguen Viet Nhung3, and Byung Jun Min4

Affiliations

1Medical Physics and Research Department, Hong Kong Sanatorium & Hospital,
2 Village Road, Happy Valley, Hong Kong, China
2Division of Artificial Intelligent Clinical Research, Radisen,
128, Gongduk B/D, Saechang-ro, Mapo-gu, Seoul, Korea
3National Tuberculosis Programme, Vietnam National Lung Hospital,
Hanoi, Vietnam
4Department of Radiation Oncology, Chungbuk National University Hospital,
776, 1 Sunhwan-ro, Seowon-gu, Cheongju, Korea

Abstract

A deep learning-based binary classifier was proposed to diagnose tuberculosis (TB) and non-TB disease using a chest X-ray radiograph. The proposed classifier comprised two-step binary decision trees, each trained by a deep learning model with convolution neural network (CNN) based on the PyTorch frame. Normal and abnormal images of chest X-ray was classified in the first step. The abnormal images were predicted to be classified into TB and non-TB disease by the second step of the process. The accuracies of first and second step were 98% and 80% respectively. Moreover, re-training could improve the stability of prediction accuracy for images in different data groups.

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