Abstract
Classification of diseases from biomedical images is a fast growing emerging field of research. In this regard, chest X-Rays (CXR) are one of the most widely used medical images to diagnose common heart and lung diseases where previous works have explored the usage of various pre-trained deep learning models to perform the classification. However, these models are very deep, thus use large number of parameters. Moreover, it is still not possible to find readily available access to a practicing radiologist for proper diagnosis from an X-Ray image of chest. Hence, this fact motivated us to conduct this research with the aim to classify CXR images in an automated manner with smaller number of parameters during training for 14 different categories of thoracic diseases and produce heatmap for the corresponding image in order to show the location of abnormality. For the purpose of classification, transfer learning is used with the pre-trained network of Resnet18, while the heatmaps are generated using pooling along the channel dimension and then computing the average of class-wise features. The proposed model contains less parameters to train and provides better performance than the other models present in the literature. The trained model is then validated both quantitatively and visually by producing localized images in the form of heatmaps of the CXR images. Moreover, the dataset and code of this work are provided online (http://www.nitttrkol.ac.in/indrajit/projects/deeplearning-chestxray/).
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
Similar content being viewed by others
Notes
References
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Bradski, G.: The OpenCV Library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)
Collard, H., et al.: Acute exacerbations of idiopathic pulmonary fibrosis. Am. J. Respir. Crit. Care Med. 176(7), 636–643 (2007)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database, pp. 248–255 (2009)
Eriksson, S.: Pulmonary emphysema and alpha1-antitrypsin deficiency. Acta Med. Scand. 175(2), 197–205 (1964)
Fine, M., et al.: A prediction rule to identify low-risk patients with community-acquired pneumonia. N. Engl. J. Med. 336(4), 243–250 (1997)
Guendel, S., et al.: Learning to recognize abnormalities in chest x-rays with location-aware dense networks. arXiv preprint arXiv:1803.04565 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.: Densely connected convolutional networks, pp. 2261–2269 (2017)
Kinga, D., Adam, J.: A method for stochastic optimization (2015)
Li, Z., et al.: Thoracic disease identification and localization with limited supervision. arXiv preprint arXiv:1711.06373 (2017)
Light, R., Macgregor, M.I., Luchsinger, P., Ball, W.: Pleural effusions: the diagnostic separation of transudates and exudates. Ann. Internal Med. 77(4), 507–513 (1972)
Oliphant, T.: Python for scientific computing. Comput. Sci. Eng. 9(3), 10–20 (2007)
Ost, D., Fein, A.M., Feinsilver, S.: The solitary pulmonary nodule. N. Engl. J. Med. 348(25), 2535–2542 (2003)
Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS-W (2017)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Rajpurkar, P., et al.: Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
Reeder, W., Goodrich, B.: Pulmonary infiltration with eosinophilia (PIE syndrome). Ann. Internal Med. 36(5), 1217–1240 (1952)
Robin, E., Cross, C., Zelis, R.: Pulmonary edema. N. Engl. J. Med. 288(6), 292–304 (1973)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision, pp. 2818–2826 (2016)
Targhetta, R., Chavagneux, R., Bourgeois, J., Dauzat, M., Balmes, P., Pourcelot, L.: Sonographic approach to diagnosing pulmonary consolidation. J. Ultrasound Med. 11(12), 667–672 (1992)
Tavora, F., et al.: Cardiomegaly is a common arrhythmogenic substrate in adult sudden cardiac deaths, and is associated with obesity. Pathology 44(3), 187–191 (2012)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summer, R.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, pp. 3462–3471 (2017)
Weissberg, D., Refaely, Y.: Hernia of the lung. Ann. Thoracic Surg. 74(6), 1963–1966 (2002)
Wu, R., Yang, P., Kuo, S., Luh, K.: “Fluid color” sign: a useful indicator for discrimination between pleural thickening and pleural effusion. J. Ultrasound Med. 14(10), 767–769 (1995)
Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501 (2017)
Acknowledgment
This work has been co-supported by the Polish National Science Centre (2014/15/ B/ST6/05082), Foundation for Polish Science (TEAM to DP) and by the grant from the Department of Science and Technology, India under Indo-Polish/Polish-Indo project No.: DST/INT/POL/P-36/2016. The work was co-supported by grant 1U54DK107967-01 Nucleome Positioning System for Spatiotemporal Genome Organization and Regulation within 4DNucleome NIH program.
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
Rakshit, S., Saha, I., Wlasnowolski, M., Maulik, U., Plewczynski, D. (2019). Deep Learning for Detection and Localization of Thoracic Diseases Using Chest X-Ray Imagery. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-20915-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20914-8
Online ISBN: 978-3-030-20915-5
eBook Packages: Computer ScienceComputer Science (R0)