Automated Detection of Abnormalities in Chest X-Ray Images Using Convolutional Neural Networks

  IJPTT-book-cover
 
International Journal of P2P Network Trends and Technology (IJPTT)          
 
© 2018 by IJPTT Journal
Volume-8 Issue-2
Year of Publication : 2018
Authors : K. Aravinth Raaj, B. Joel Sherwin, S. Sabeetha Saraswathi
DOI :  10.14445/22492615/IJPTT-V8I2P404

Citation

K. Aravinth Raaj, B. Joel Sherwin, S. Sabeetha Saraswathi "Automated Detection of Abnormalities in Chest X-Ray Images Using Convolutional Neural Networks". International Journal of P2P Network Trends and Technology (IJPTT).V8 (2) 18-24 March to April 2018. ISSN:2249-2615. www.ijpttjournal.org. Published by Seventh Sense Research Group.

Abstract

Heart and lung diseases account for 11% of total deaths in India, with the cumulative count of all chest related deaths accounting for almost 30% of the world`s mortality rate. A computer-aided diagnosis system is proposed that utilizes Convolutional Neural Networks (CNN) to classify images of chest x-rays into normal or abnormal labels, and if abnormal, attempts to identify the kind of abnormality/disease further. We trained Convolutional Neural Network (CNN) to classify tuberculosis, pneumonia, and cardiovascular abnormalities. The pre-trained neural network GoogLeNet is tuned using a dataset of labeled x-rays, and the model is used to classify an x-ray without prior knowledge in medicine. We extend our work further to classify all 11 diseases.

References

[1] B. van Ginneken, S. Katsuragawa, B. M. ter Haar Romeny, K. Doi, and M. A. Viergever, "Automatic detection of abnormalities in chest radiographs using local texture analysis," IEEE transactions on medical imaging, vol. 21, no. 2, pp. 139–149, 2002.
[2] L. Hogeweg, C. Mol, P. A. de Jong, R. Dawson, H. Ayles, and B. van Ginneken, "Fusion of local and global detection systems to detect tuberculosis in chest radiographs," in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2010, pp. 650–657.
[3] Hooda, R., Sofat, S., Kaur, S., Mittal, A., & Meriaudeau, F. (2017). Deep-learning: "A potential method for tuberculosis detection using chest radiography," IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 497–502, 2017.
[4] Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ng, A. Y. (2017). “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” in arXiv: 1711.05225v2 [cs.CV], 2017.
[5] Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., & Lyman, K. (2017). "Learning to diagnose from scratch by exploiting dependencies among labels," in arXiv: 1710.10501 [cs.CV], PP.1–12, 2018
[6] Raoof, S., Feigin, D., Sung, A., Raoof, S., Irugulpati, L., & Rosenow, E. C. (2012). "Interpretation of plain chest roentgenogram," IEEE Computer vision Foundation, Pp. 545–558, 2012
[7] Schalekamp S, Van Ginneken B, Karssemeijer N, Schaefer-Prokop C. "Chest radiography: New technological developments and their applications. Seminars in respiratory and critical care medicine". Thieme Medical Publishers, 2014
[8] Rajkomar, A., Lingam, S., Taylor, A. G., Blum, M., & Mongan, J. (2017). "High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks," Journal of Digital Imaging, pp.95–101, 2017.
[9] A. Krizhevsky and G. Hinton, "Learning multiple layers of features from tiny images," 2009
[10] S. Jaeger, A. Karargyris, S. Candemir, L. Folio, J. Siegelman, F. Callaghan, Z. Xue, K. Palaniappan, R. K. Singh, S. Antani et al., "Automatic tuberculosis screening using chest radiographs," IEEE transactions on medical imaging, vol. 33, no. 2, pp. 233–245, 2014.
[11] Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
[12] T. Xu, I. Cheng, R. Long, and M. Mandal, "Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs," EURASIP Journal on Image and Video Processing, vol. 2013, no. 1, p. 1, 2013.
[13] A. Karargyris, J. Siegelman, D. Tzortzis, S. Jaeger, S. Candemir, Z. Xue, K. Santosh, S. Vajda, S. Antani, L. Folio, et al., "Combination of texture and shape features to detect pulmonary abnormalities in digital chest x- rays," International Journal of computer-assisted radiology and surgery, vol. 11, no. 1, pp. 99–106, 2016.
[14] Sagnik M, Ramaprasad P, "Comparative Study of Convolutional Neural Networks" SSRG International Journal of Electronics and Communication Engineering 6.8 (2019): 18-21.
[15] J. M. Wolterink, T. Leiner, M. A. Viergever, and I. Isgum, "Automatic coronary calcium scoring in cardiac ct angiography using convolutional neural networks," Medical Image Analysis, vol. 9349, pp. 589–596, 2016.
[16] H. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. Summers, "Improving computer-aided detection using convolutional neural networks and random view aggregation." IEEE Transactions on Medical Imaging, 2015
[17] H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, "Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics, and transfer learning," IEEE Transactions on Medical Imaging, vol. 35, no. 5, p. 1285, 2016
[18] S. Yang, W. Cai, H. Huang, Z. Yun, W. Yue, and D. D. Feng, "Locality-constrained subcluster representation ensemble for lung image classification," Medical Image Analysis, vol. 22, no. 1, pp. 102–113, 2015.
[19] ChangmiaoWang, Ahmed Elazab, JianhuangWu, and Qingmao Hu. Lung Nodule Classification Using Deep Feature Fusion in Chest Radiography. Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society, 57:10– 18, Nov 2017.
[20] Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gram- fort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Ga¨elVaroquaux. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pages 108–122, 2013.
[21] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567, 2015.
[22] P. Maduskar, R. H. Philipsen, J. Melendez, E. Scholten, D. Chanda, H. Ayles, C. I. S´anchez, and B. van Ginneken, “Automatic detection of pleural effusion in chest radiographs,” Medical image analysis, vol. 28, pp. 22–32, 2016.
[23] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Du- mitru Erhan, Vincent Vanhoucke, and Andrew Ra- binovich. Going deeper with convolutions. CoRR, abs/1409.4842, 2014.
[24] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015.

Keywords
neural networks; deep learning; medical imaging; computer-aided diagnosis.