Browse > Article
http://dx.doi.org/10.7742/jksr.2021.15.4.455

Evaluation of Classification Performance of Inception V3 Algorithm for Chest X-ray Images of Patients with Cardiomegaly  

Jeong, Woo-Yeon (Department of Biomedical Engineering, Kyungpook National University)
Kim, Jung-Hun (Bio-Medical Research institute, Kyungpook National University Hospital)
Park, Ji-Eun (Nonlinear Dynamics Research Center, Kyungpook National University)
Kim, Min-Jeong (Department of Biomedical Engineering, Kyungpook National University)
Lee, Jong-Min (Department of Radiology, School of Medicine, Kyungpook National University)
Publication Information
Journal of the Korean Society of Radiology / v.15, no.4, 2021 , pp. 455-461 More about this Journal
Abstract
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.
Keywords
Cardiomegaly; Artificial Intelligence; Deep learning; Chest X-ray Image; Classification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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
2 H. J. Moon, E. K. Kim, J. S. Park, J. Y. Kwak, "Thyroid Ultrasound: Change of Inter-observer Variability and Diagnostic Performance after Training", Journal of Korean Society of Ultrasound in Medicine, Vol. 30, No. 1, pp. 23-28, 2011.
3 E. D. Frohlich, "Left ventricular hypertrophy as a risk factor", Cardiology Clinics, Vol. 4, No. 1, pp. 137-144, 1986.   DOI
4 S. Lim, M. Lee, "A Computer Aided Diagnosis Algorithm for Classification of Malignant Melanoma Based on Deep Learning", Journal of the Korea Society of Digital Industry and Information Management, Vol. 14, No. 4, pp. 69-77, 2018.   DOI
5 Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning", arXiv:1711.05525, 2017. https://arxiv.org/abs/1711.05225v3
6 L. Yao, E. Poblenz, D. Dagunts, B. Covington, D. Bernard, K. Lyman, "Learning to diagnose from scratch by exploiting dependencies among labels", arXiv preprint arXiv:1710.10501. 2017. https://arxiv.org/abs/1710.10501
7 X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers, "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases", In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097-2106, 2017. https://doi.org/10.1109/CVPR.2017.369
8 J. C. Anderson, H. A. Baltaxe, G. L. Wolf, "Inability to show clot: one limitation of ultrasonography of the abdominal aorta", Radiology, Vol. 132, No. 3, pp. 693-696, 1979. http://dx.doi.org/10.1148/132.3.693   DOI
9 E. F. Philbin, R. Garg, K. Danisa, D. M. Denny, G. Gosselin, C. Hassapoyannes, "The Relationship Between Cardiothoracic Ratio and Left Ventricular Ejection Fraction in Congestive Heart Failure", Archives of Internal Medicine, Vol. 158, No. 5, pp. 501-506, 1998. http://dx.doi.org/10.1001/archinte.158.5.501   DOI
10 J. Y. Kim, S. Y. Ye, "Diagnostic Classification of Chest X-ray Pneumonia using Inception V3 Modeling", Journal of the Korean Society of Radiology, Vol. 14, No. 6, pp. 773-780, 2020. https://doi.org/10.7742/jksr.2020.14.6.773   DOI
11 Google. Advanced Guide to Inception v3 on Cloud TPU. https://cloud.google.com/tpu/docs/inception-v3-advanced?hl=en
12 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, A. Rabinovich, "Going deeper with convolutions", In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015. https://arxiv.org/abs/1409.4842
13 M. S. Ko, B. C. Jeong, D. G. Kim, C. Han, "Deep Learning Under Privileged Information for Pneumonia Detection", The Institute of Electronics and Information Engineers, Vol. 58, No. 3, pp. 67-73, 2021. https://arxiv.org/abs/1805.11614
14 H. J. Song, E. B. Lee, H. J. Jo, S. Y. Park, S. Y. Kim, H. J. Kim, J. W. Hong, "Evaluation of Classification and Accuracy in Chest X-ray Images using Deep Learning with Convolution Neural Network", Journal of the Korean Society of Radiology, Vol. 14, No. 1, pp. 39-44, 2020. https://doi.org/10.7742/jksr.2019.14.1.39   DOI
15 C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, "Rethinking the Inception Architecture for Computer Vision", arXiv preprint arXiv:1512.00567. 2015.
16 J. K. Lee, S. J. Kim, N. J. Kwak, D. W. Kim, J. H. Ahn, "A Deep Learning Model for Judging Presence or Absence of Lesions in the Chest X-ray Images", The Journal of the Korean Institute of Information and Communication Engineering, Vol. 24, No. 2, pp. 212-218, 2020. https://doi.org/10.6109/jkiice.2020.24.2.212   DOI
17 D. Levy, K. M. Anderson, D. D. Savage, W. B. Kannel, J. C. Christiansen, W. P. Castelli, "Echocardiographically detected left ventricular hypertrophy: prevalence and risk factors: the Framingham Heart Study", Annals of Internal Medicine, Vol. 108, No. 1, pp. 7-13, 1988. https://doi.org/10.7326/0003-4819-108-1-7   DOI