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An Ensemble Algorithm Based on Deep Learning for Tuberculosis Classification

  • Alfonso Hernández
  • Ángel PanizoEmail author
  • David Camacho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

Abstract

In the past decades the field of Artificial Intelligence, and specially the Machine Learning (ML) research area, has undergone a great expansion. This has been allowed for the greater availability of data, which has not been foreign in the field of medicine. This data can be used to train supervised Machine Learning algorithms. Taking into account that this data can be in form of images, several ML algorithms, such as Artificial Neural Networks, Support Vector Machines, or Deep Learning Algorithms, are particularly suitable candidates to help in medical diagnosis. This works aims to study the automatic classification of X-Ray images among patients who may have tuberculosis, using an ensemble approach based on ML. In order to achieve this, an ensemble classifier, based on three pre-trained Convolutional Neural Networks, has been designed. A set of 800 samples with chest X-Ray images will be used to carry out an experimental analysis of our proposed ensemble-based classification method.

Keywords

Deep Learning Convolutional Neural Networks Support vector machines Image classification 

References

  1. 1.
    Martín, A., Lara-Cabrera, R., Camacho, D.: Android malware detection through hybrid features fusion and ensemble classifiers: the AndroPyTool framework and the OmniDroid dataset. Inf. Fusion 52, 128–142 (2019)CrossRefGoogle Scholar
  2. 2.
    Martín, A., Menéndez, H.D., Camacho, D.: MOCDroid: multi-objective evolutionary classifier for android malware detection. Soft Comput. 21(24), 7405–7415 (2017)CrossRefGoogle Scholar
  3. 3.
    Kermany, D.S.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131.e9 (2018)CrossRefGoogle Scholar
  4. 4.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017) CrossRefGoogle Scholar
  5. 5.
    Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, April 2015Google Scholar
  6. 6.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep Learn. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  7. 7.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)Google Scholar
  9. 9.
    LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256. IEEE (2010)Google Scholar
  10. 10.
    Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2–3), 427–436 (2008)CrossRefGoogle Scholar
  11. 11.
    Shin, H.-C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)CrossRefGoogle Scholar
  12. 12.
    Wang, J., Perez, L.: The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw. Vis. Recognit. (2017) Google Scholar
  13. 13.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  14. 14.
    Martín, A., Lara-Cabrera, R., Fuentes-Hurtado, F., Naranjo, V., Camacho, D.: EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation. J. Parallel Distrib. Comput. 117, 180–191 (2018)CrossRefGoogle Scholar
  15. 15.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2016Google Scholar
  17. 17.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2016Google Scholar
  18. 18.
    Fierrez, J., Morales, A., Vera-Rodriguez, R., Camacho, D.: Multiple classifiers in biometrics Part 1: fundamentals and review. Inf. Fusion 44, 57–64 (2018)CrossRefGoogle Scholar
  19. 19.
    Del Ser, J., et al.: Bio-inspired computation: where we stand and what’s next. Swarm Evol. Comput. 48, 220–250 (2019)CrossRefGoogle Scholar
  20. 20.
    Boyat, A.K., Joshi, B.K.: A review paper: noise models in digital image processing. Signal Image Process. Int. J. 6(2), 63–75 (2015)CrossRefGoogle Scholar
  21. 21.
    Antani, S., Wáng, Y.X., Lu, P.X., Thoma, G., Jaeger, S., Candemir, S.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4, 252–477 (2014)Google Scholar
  22. 22.
    Vayena, E., Blasimme, A., Cohen, I.G.: Machine learning in medicine: addressing ethical challenges. PLOS Med. 15(11), e1002689 (2018)CrossRefGoogle Scholar
  23. 23.
    Montavon, G., Samek, W., Müller, K.-R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1–15 (2018)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alfonso Hernández
    • 1
  • Ángel Panizo
    • 1
    Email author
  • David Camacho
    • 2
  1. 1.Computer Science DeparmentUniversidad Autónoma de MadridMadridSpain
  2. 2.Information Systems DepartmentTechnical University of MadridMadridSpain

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