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http://dx.doi.org/10.6109/jkiice.2020.24.2.212

A Deep Learning Model for Judging Presence or Absence of Lesions in the Chest X-ray Images  

Lee, Jong-Keun (Department of Information and Communication Engineering, Chung-buk National University)
Kim, Seon-Jin (Department of Information and Communication Engineering, Chung-buk National University)
Kwak, Nae-Joung (Department of Information and Communication Engineering, Chung-buk National University)
Kim, Dong-Woo (CELLGENTEK CO. LTD)
Ahn, Jae-Hyeong (Department of Information and Communication Engineering, Chung-buk National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.2, 2020 , pp. 212-218 More about this Journal
Abstract
There are dozens of different types of lesions that can be diagnosed through chest X-ray images, including Atelectasis, Cardiomegaly, Mass, Pneumothorax, and Effusion. Computed tomography(CT) test is generally necessary to determine the exact diagnosis and location and size of thoracic lesions, however computed tomography has disadvantages such as expensive cost and a lot of radiation exposure. Therefore, in this paper, we propose a deep learning algorithm for judging the presence or absence of lesions in chest X-ray images as the primary screening tool for the diagnosis of thoracic lesions. The proposed algorithm was designed by comparing various configuration methods to optimize the judgment of presence of lesions from chest X-ray. As a result, the evaluation rate of lesion presence of the proposed algorithm is about 1% better than the existing algorithm.
Keywords
Artificial neural network; Convolutional neural network; Deep learning; Pneumonia; Pneumothorax;
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