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KMID : 1151820210150040547
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Journal of the Korean Society of Radiology
2021 Volume.15 No. 4 p.547 ~ p.554
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Comparative Analysis by Batch Size when Diagnosing Pneumonia on Chest X-Ray Image using Xception Modeling
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Kim Ji-Yul
Ye Soo-Young
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Abstract
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In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.
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KEYWORD
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Deep learning , batch size , pneumonia , Pneumonia , Automatic Diagnosis of Pneumonia , chest X-ray imaging
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