Browse > Article
http://dx.doi.org/10.7742/jksr.2022.16.4.411

Effectiveness of the Detection of Pulmonary Emphysema using VGGNet with Low-dose Chest Computed Tomography Images  

Kim, Doo-Bin (Korea Medical Institute)
Park, Young-Joon (Department of Radiologic technology, Cheju halla University)
Hong, Joo-Wan (Department of Radiological Science, College of Health Sciences, Eulji University)
Publication Information
Journal of the Korean Society of Radiology / v.16, no.4, 2022 , pp. 411-417 More about this Journal
Abstract
This study aimed to learn and evaluate the effectiveness of VGGNet in the detection of pulmonary emphysema using low-dose chest computed tomography images. In total, 8000 images with normal findings and 3189 images showing pulmonary emphysema were used. Furthermore, 60%, 24%, and 16% of the normal and emphysema data were randomly assigned to training, validation, and test datasets, respectively, in model learning. VGG16 and VGG19 were used for learning, and the accuracy, loss, confusion matrix, precision, recall, specificity, and F1-score were evaluated. The accuracy and loss for pulmonary emphysema detection of the low-dose chest CT test dataset were 92.35% and 0.21% for VGG16 and 95.88% and 0.09% for VGG19, respectively. The precision, recall, and specificity were 91.60%, 98.36%, and 77.08% for VGG16 and 96.55%, 97.39%, and 92.72% for VGG19, respectively. The F1-scores were 94.86% and 96.97% for VGG16 and VGG19, respectively. Through the above evaluation index, VGG19 is judged to be more useful in detecting pulmonary emphysema. The findings of this study would be useful as basic data for the research on pulmonary emphysema detection models using VGGNet and artificial neural networks.
Keywords
Low-dose chest CT; Emphysema; Convolutional Neural Network; VGGNet;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. H. Kassania, P. H. Kassanib, M. J. Wesolowskic, K. A. Schneidera, R. Detersa, "Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach", Biocybernetics and Biomedical Engineering, Vol. 41, No. 3, pp. 867-879, 2021. http://dx.doi.org/10.1016/j.bbe.2021.05.013   DOI
2 D. J. Brenner, E. J. Hall, "Computed tomography-an increasing source of radiation exposure", New England journal of medicine, Vol. 357, No. 22, pp. 2277-2284, 2007. https://doi.org/10.1056/NEJMra072149   DOI
3 P. C. A. Jacobs, W. P. Th. M. Mali, D. E. Grobbee, Y. v. d. Graaf, "Prevalence of incidental findings in computed tomographic screening of the chest: a systematic review", Journal of Computer Assisted Tomography, Vol. 32, No. 2, pp. 214-221, 2008. http://dx.doi.org/10.1097/RCT.0b013e3181585ff2   DOI
4 G. L. Snider, J. Kleinerman, W. M. Thurlbeck, Z. H. Bengali, "The definition of emphysema. Report of a National Heart, Lung, and Blood Institute, Division of Lung Diseases workshop", American Review of Respiratory Disease, Vol. 132, No. 1, pp. 182-185, 1985. https://doi.org/10.1164/arrd.1985.132.1.182   DOI
5 M. Rastgarpour, J. Shanbehzadeh, "Application of AI Techniques in Medical Image Segmentation and Novel Categorization of Available Methods and Tools", Lecture Notes in Engineering and Computer Science, Vol. 2188, No. 1, pp. 519-523, 2011.
6 N. Braman, D. Beymer, E. Dehghan, "Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network", arXiv, 2018. https://doi.org/10.48550/arXiv.1812.01087   DOI
7 K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition", arXiv, 2014. https://doi.org/10.48550/arXiv.1409.1556   DOI
8 X. Glorot, Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks", Journal of Machine Learning Research, Vol. 9, pp. 249-256, 2010.
9 L. Yao, E. Poblenz, D. Dagunts, B. Covington, D. Bernard, K. Lyman, "Learning to diagnose from scratch by exploiting dependencies among labels", arXiv, 2017. https://doi.org/10.48550/arXiv.1710.10501   DOI
10 U. Niyaz, A. S. Sambyal, Devanand, "Advances in Deep Learning Techniques for Medical Image Analysis," 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 271-277, 2018. https://doi.org/10.1109/PDGC.2018.8745790   DOI
11 G. Bortsova, F. Dubost, S. Orting, I. Katramados, L. Hogeweg, L. Thomsen, M. Wille, M. d. Bruijne, "Deep learning from label proportions for emphysema quantification", International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 11071, pp. 768-776, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_85   DOI
12 S. M. Humphries, A. M. Notary, J. P. Centeno, M. J. Strand, J. D. Crapo, E. K. Silverman, D. A. Lynch, "Deep learning enables automatic classification of emphysema pattern at CT", Radiology, Vol. 294, No. 2, pp. 434-444, 2020. http://dx.doi.org/10.1148/radiol.2019191022   DOI
13 Y. Bengio, P. Simard, P. Frasconi, "Learning long-term dependencies with gradient descent is difficult", IEEE Transactions on Neural Networks, Vol. 5, No. 2, pp. 157-166, 1994. https://doi.org/10.1109/72.279181   DOI
14 X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri and R. M. Summers, "ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases", 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097-2106, 2017. https://doi.org/10.1109/CVPR.2017.369   DOI
15 P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, M. P. Lungren, A. Y. Ng, "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning", arXiv, 2017. https://doi.org/10.48550/arXiv.1711.05225   DOI
16 J. C. M. van de Wiel, Y. Wang, D. M. Xu, H. J. van der Zaag-Loonen, E. J. van der Jagt, R. J. van Klaveren, M. Oudkerk, "Neglectable benefit of searching for incidental findings in the Dutch--Belgian lung cancer screening trial (NELSON) using low-dose multidetector CT", European radiology, Vol. 17, No. 6, pp. 1474-1482, 2007. http://dx.doi.org/10.1007/s00330-006-0532-7   DOI
17 E. J. Stern, M. S. Frank, "CT of the lung in patients with pulmonary emphysema: diagnosis, quantification, and correlation with pathologic and physiologic findings", American Journal of Roentgenology, Vol. 162, No. 4, pp. 791-798, 1994. http://dx.doi.org/10.2214/AJR.162.4.8140992   DOI