Abstract
The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions of some lung diseases by computer-aided diagnosis (CADx) based on the convolutional neural network (CNN) are presented for the largest open CXR dataset with radiologist-labeled reference standard evaluation sets (CheXpert). The results demonstrate the lower validation loss and higher area under curve (AUC) values for the receiver operating characteristic curve (ROC) for the models with lung mask segmentation (for 4 from 14 lung diseases) and data augmentation (for 10 from 14 lung diseases) for small image sizes (\(320\times 320\) pixels) and standard CNN (like DenseNet121) even. Moreover, the additional training leads to the lower validation loss and higher AUC values for the model with data augmentation. The further progress of CADx is assumed to be obtained for the big datasets with the bigger original image sizes by longer training with the tuned data augmentation.
The work was partially supported by Huizhou Science and Technology Bureau and Huizhou University (Huizhou, P.R.China) in the framework of Platform Construction for China-Ukraine Hi-Tech Park Project.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shen, D., Wu, G., Suk, H.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Smistad, E., et al.: Medical image segmentation on GPUs - a comprehensive review. Med. Image Anal. 20(1), 1–18 (2015)
Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
Irvin, J., et al.: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv preprint arXiv:1901.07031 (2019)
Gordienko, Y., et al.: Deep Learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 638–647. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91008-6_63
Peng, G., et al.: Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer. In: Proceedings of 10th International Conference on Advanced Computational Intelligence, ICACI 2018, pp. 878–883. IEEE (2018)
Stirenko, S., et al.: Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation. In: Proceedings of IEEE 38th International Conference on Electronics and Nanotechnology, pp. 422–428. IEEE (2018)
Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174, 71–74 (2000)
Armato, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Jaeger, S., et al.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475–477 (2014)
Wang, X., et al.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2106. IEEE (2017). arXiv preprint arXiv:1705.02315
van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med. Image Anal. 10(1), 19–40 (2006)
Hashemi, A., Pilevar, A.H.: Mass detection in lung CT images using region growing segmentation and decision making based on fuzzy systems. Int. J. Image Graph. Signal Process. 6(1), 1–8 (2013)
Juhász, S., Horváth, Á., Nikházy, L., Horváth, G., Horváth, Á.: Segmentation of anatomical structures on chest radiographs. In: Bamidis, P.D., Pallikarakis, N. (eds.) XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. IFMBE Proceedings, vol. 29, pp. 359–362. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13039-7_90
Kochura, Yu., Gordienko, Yu., Stirenko, S., et al.: Aggressive data augmentation and segmentation for lung disease diagnostics by deep learning (2019, submitted)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Chollet, F.: Deep Learning with Python. Manning Publications, New York (2018)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint:1603.04467 (2016)
Gordienko, N., Lodygensky, O., Fedak, G., Gordienko, Yu.: Synergy of volunteer measurements and volunteer computing for effective data collecting, processing, simulating and analyzing on a worldwide scale. In: Proceedings of the IEEE 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 193–198. IEEE (2015)
Rather, N.N., Patel, C.O., Khan, S.A.: Using deep learning towards biomedical knowledge discovery. Int. J. Math. Sci. Comput. 3(2), 1–10 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gang, P. et al. (2019). Effect of Data Augmentation and Lung Mask Segmentation for Automated Chest Radiograph Interpretation of Some Lung Diseases. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-36808-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36807-4
Online ISBN: 978-3-030-36808-1
eBook Packages: Computer ScienceComputer Science (R0)