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A Novel Approach for Tuberculosis Screening Based on Deep Convolutional Neural Networks

Sangheum Hwang et al. — SPIE Medical Imaging 2016
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We propose an automatic TB screening system based on deep CNN. Since CNN extracts the most discriminative features according to target objective from given data by itself, the proposed system does not require manually-designed features for TB screening. Also, we show that transfer learning from lower convolutional layers of pre-trained networks resolves the difficulties in handling high-resolution medical images and training huge parameters with limited number of images. Experiments are conducted using three real field datasets, the KIT, MC and Shenzhen sets, and the results show that the proposed system has high screening performance in terms of AUC and accuracy.

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AUTHORS

Sangheum Hwang1, Hyo-Eun Kim1, Jihoon Jeong2 and Hee-Jin Kim3

1Lunit Inc., 2Kyung Hee University, 3Korean Institute of Tuberculosis

PUBLISHED
SPIE Medical Imaging 2016

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