Browse > Article
http://dx.doi.org/10.7742/jksr.2021.15.4.547

Comparative Analysis by Batch Size when Diagnosing Pneumonia on Chest X-Ray Image using Xception Modeling  

Kim, Ji-Yul (Daewoo general hospital)
Ye, Soo-Young (Department of Radiology Catholic University of the Pusan)
Publication Information
Journal of the Korean Society of Radiology / v.15, no.4, 2021 , pp. 547-554 More about this Journal
Abstract
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.
Keywords
Deep learning; batch size; pneumonia; Pneumonia; Automatic Diagnosis of Pneumonia; chest X-ray imaging;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. E. Park, "Coronavirus Infectious Disease-19 virus (SARS-CoV-2) characteristics, transmission and clinical picture", Pediatric Infection & Vaccine, pp. 27, 2019.
2 N. Petrosillo, G. Viceconte, O. Ergonul, G. Ippolito, E. Petersen, "COVID-19 SARS and MERS: are they closely related?", Clinical Microbiology and Infection, Vol. 26, No. 6, pp. 729-734, 2020. http://dx.doi.org/10.1016/j.cmi.2020.03.026   DOI
3 K. W. Y ang, J. S. Kang, H. N. Lee, "Digital Walkie-Talkie Identification scheme based on Convolutional Neural Network", The Journal of Korean Institute of Communications and Information Sciences, pp. 1210-1211, 2018.
4 F. Chollet, "Xception: Deep learning with depthwise separable convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-8, 2017.
5 J. Almirall, I. Bolibar, J. Vidal, "Epidemiology of community-acquired pneumonia in adults : a population-based study", European respiratory journal, Vol. 15, No. 4, pp. 757-763, 2000.   DOI
6 A. Krizhevsky, G. Hinton, I. Sutskever, "ImageNet classifi cation with deep convolutional neural networks," Advances in Neural Information Processing (NIPS), pp. 1-9, 2012.
7 H. S. Kim, "Classification and Combination of Fashion Items Using CNN-Based Deep Learning", Journal of Digital Contents Society, Vol. 22, No. 3, pp. 475-482, 2021.   DOI
8 B. H. Kang, Y. K. Choi, C. Y. Jeon, "The Effects of Qingfei Paidu Decoction on Coronavirus Disease-19 : A Narrative Review", The Journal of Internal Korean Medicine, Vol. 41, No. 3, pp. 424-433, 2020.   DOI
9 M. G. Kim, J. H. Yun, Y. W. Cho, "Deep learning in medical imaging", Neurospine, Vol. 16, pp. 657-668, 2019.   DOI
10 J. H. Kim "Imaging informatics : a new horizon for radiology in the era of artificial intelligence, big data, and data science", Korean Journal of Radiology, Vol. 80, pp. 176-201, 2019. http://dx.doi.org/10.3348/jksr.2019.80.2.176   DOI
11 https://eda-ai-lab.tistory.com/13
12 https://hwiyong.tistory.com/290
13 https://sotudy.tistory.com/14
14 I. Kandel, C. Mauro, "The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset", ICT express, Vol. 6, No. 4, pp. 312-315, 2020. http://dx.doi.org/10.1016/j.icte.2020.04.010   DOI
15 K. D. Song, M. C. Kim, S. H. Do, "The Latest Trends in the Use of Deep Learning in Radiology Illustrated Through the Stages of Deep Learning Algorithm Development", Korean Journal of Radiology, Vol. 80, No. 2, pp. 202-212, 2019. http://dx.doi.org/10.3348/jksr.2019.80.2.202   DOI
16 J. Warrens, "Cohen's kappa can always be increased and decreased by combining categories", Statistical Methodology, Vol. 7, No. 6, pp. 673-677, 2010. http://dx.doi.org/10.1016/j.stamet.2010.05.003   DOI
17 P. Cedric, P. Matthew, E. Nicole, "acute respiratory syndrome-related coronavirus 2 : a narrative review", Annals of internal medicine, Vol. 172, No. 11, pp. 726-734, 2020.   DOI
18 G. D. Rubin, C. J. Ryerson, L. B. Haramati, "The role of chest imaging in patient management during the COVID-19 pandemic", Radiology, Vol. 158, pp. 106-118 2020.
19 H. S. Lee, M. S. Park, J. M. Kim, "Deep Learning in Medical Imaging", Korean society of imaging information in medicine, Vol. 20, No. 1, pp. 13-18, 2014.
20 R. Jain, M. Gupta, S. Taneja, "Deep learning based detection and analysis of COVID-19 on chest X-ray images", Applied Intelligence, Vol. 51, No. 3, pp. 1690-1700, 2021.   DOI
21 S. J. Kim, J. C. Yu, "COVID-19 Chest X-ray reading Technique based on Deep Learning", Korean society of computer and information, Vol. 29, No. 3, pp. 31-32, 2021.
22 B. H. Choi, Y. J. Kim, S. J. Choi, K. G. Kim, "Malignant and Benign Classification of Liver Tumor in CT according to Data pre-processing and Deep- running model", Journal of Biomedical Engineering Research, Vol. 39, pp. 229-236, 2018. https://doi.org/10.9718/JBER.2018.39.6.229   DOI
23 https://keras.io/ko/
24 S. M. Lee, "Short-term Power Consumption Forecasting Based on IoT Power Meter with LSTM and GRU Deep Learning", The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 19, No. 3, pp. 79-85, 2019.