Abstract
X-rays are the most commonly used medical images and are involved in all areas of healthcare because they are relatively inexpensive compared to other modalities and can provide sensitive results. The interpretation by the radiologist, however, can be challenging because it depends on his experience and a clear mind. There is also a lack of specialized physicians, mainly in the least developed areas, which increases the need for alternatives to X-ray analysis. Recent research shows that the development of Deep Learning based methods for chest X-rays analysis has the potential to replace the radiologists analysis in the future. However, most of the published DL algorithms were developed to classify a single disease. We propose an ensemble of Deep Neural Networks that can classify several classes. In this work, the network was used to classify five chest diseases: Atelectasis, Cardiomegaly, Consolidation, Edema, and Pleural Effusion. An AUC of 0.96 was achieved with the training data and 0.74 with the test data.
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
References
Hill, D.L., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Phys. Med. Biol. 46, 1–45 (2001)
NHS England. Diagnostic Imaging Dataset. https://www.england.nhs.uk/statistics/statistical-work-areas/diagnostic-imaging-dataset/. Accessed 17 Apr 2019
Benseler, J.: A pocket guide to medical imaging. In: The Radiology Handbook. Ohio University Press, Ohio (2006)
Waseda, Y., Matsubara, E., Shinoda, K.: X-ray Diffraction Crystallography: Introduction, Examples and Solved Problems. Springer, Heidelberg (2011)
Ballard, D., Sklansky, J.: Tumor detection in radiographs. Comput. Biomed. Res. 6, 299–321 (1973)
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017)
Stephen, O., et al.: An efficient deep learning approach to pneumonia classification in healthcare. J. Healthcare Eng. 2019, 7 (2019)
Liu, C., et al.: TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE (2017)
Yates, E.J., Yates, L.C., Harvey, H.: Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. Clin. Radiol. 73(9), 827–831 (2018)
Yaniv, B., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York (2015)
Pan, I., Agarwal, S., Merck, D.: Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks. J. Digit. Imaging 32, 888–896 (2019)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR, vol. 6 (2015)
Ting, D., Cheung, C., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318, 2211–2223 (2017)
Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
Ridley, E.: AI outperforms physicians for interpreting chest x-rays. Aunt Minnie (2019)
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D., Halabi, S., Sandberg, J., Jones, R., Larson, D., Langlotz, C., Patel, B., Lungren, M., Ng, A.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Association for the Advancement of Artificial Intelligence (2019)
Clinger, N., Hunter, T., Hillman, B.: Radiology reporting: attitudes of referring physicians. In: RSNA 1988 Annual Meeting (1988)
European Society of Radiology (ESR): Good practice for radiological reporting. Guidelines from the European Society of Radiology (ESR). Insights Imaging 2, 93–96 (2011)
Stanford ML Group: CheXpert: A Large Chest X-Ray Dataset and Competition, Stanford ML Group. https://stanfordmlgroup.github.io/competitions/chexpert/. Accessed 16 Oct 2019
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861 (2017)
Nain, A.: Beating everything with Depthwise Convolution. https://www.kaggle.com/aakashnain/beating-everything-with-depthwise-convolution. Accessed 02 July 2019
ImageNet, Large Scale Visual Recognition Challenge (ILSVRC). http://www.image-net.org/challenges/LSVRC/. Accessed 18 May 2019
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: The IEEE Conference on Computer Vision and Pattern Recognition (2015)
Chen, L.-C., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Wang, S.-H., et al.: Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42(5), 85 (2018)
Rubin, J., et al.: Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks. arXiv preprint arXiv:1804.07839 (2018)
Acknowledgements
This work has been supported by FCT – Foundation for Science and Technology within the Project Scope: UIDB/00319/2020. We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ramos, A., Alves, V. (2020). A Study on CNN Architectures for Chest X-Rays Multiclass Computer-Aided Diagnosis. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1161. Springer, Cham. https://doi.org/10.1007/978-3-030-45697-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-45697-9_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45696-2
Online ISBN: 978-3-030-45697-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)