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KMID : 1151820210150040455
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Journal of the Korean Society of Radiology
2021 Volume.15 No. 4 p.455 ~ p.461
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Evaluation of Classification Performance of Inception V3 Algorithm for Chest X-ray Images of Patients with Cardiomegaly
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Jeong Woo-Yeon
Kim Jung-Hun
Park Ji-Eun
Kim Min-Jeong
Lee Jong-Min
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Abstract
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Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.
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KEYWORD
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Cardiomegaly , Artificial Intelligence , Deep learning , Chest X-ray Image , Classification
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