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http://dx.doi.org/10.7742/jksr.2021.15.5.613

Proposal of a Convolutional Neural Network Model for the Classification of Cardiomegaly in Chest X-ray Images  

Kim, Min-Jeong (Department of Biomedical Engineering, Kyungpook National University)
Kim, Jung-Hun (Bio-Medical Research institute, Kyungpook National University Hospital)
Publication Information
Journal of the Korean Society of Radiology / v.15, no.5, 2021 , pp. 613-620 More about this Journal
Abstract
The purpose of this study is to propose a convolutional neural network model that can classify normal and abnormal(cardiomegaly) in chest X-ray images. The training data and test data used in this paper were used by acquiring chest X-ray images of patients diagnosed with normal and abnormal(cardiomegaly). Using the proposed deep learning model, we classified normal and abnormal(cardiomegaly) images and verified the classification performance. When using the proposed model, the classification accuracy of normal and abnormal(cardiomegaly) was 99.88%. Validation of classification performance using normal images as test data showed 95%, 100%, 90%, and 96% in accuracy, precision, recall, and F1 score. Validation of classification performance using abnormal(cardiomegaly) images as test data showed 95%, 92%, 100%, and 96% in accuracy, precision, recall, and F1 score. Our classification results show that the proposed convolutional neural network model shows very good performance in feature extraction and classification of chest X-ray images. The convolutional neural network model proposed in this paper is expected to show useful results for disease classification of chest X-ray images, and further study of CNN models are needed focusing on the features of medical images.
Keywords
Convolutional Neural Network; Deep learning; Chest X-ray; Classification;
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