Convolutional Neural Network Architectures for the Automated Diagnosis of Celiac Disease

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10170)

Abstract

In this work, convolutional neural networks (CNNs) are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. To evaluate which network configurations are best suited for the classification of celiac disease, several different CNN networks were trained using different numbers of layers and filters and different filter dimensions. The results of the CNNs are compared with the results of popular general purpose image representations such as Improved Fisher Vectors and LBP-based methods as well as a feature representations especially designed for the classification of celiac disease. We will show that the deeper CNN architectures outperform these comparison approaches and that combining CNNs with linear support vector machines furtherly improves the classification rates for about 3–7% leading to distinctly better results (up to 97%) than those of the comparison methods.

Keywords

CNN Celiac disease Endoscopy Deep learning 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Biagi, F., Rondonotti, E., Campanella, J., Villa, F., Bianchi, P.I., Klersy, C., Franchis, R.D., Corazza, G.R.: Video capsule endoscopy and histology for small-bowel mucosa evaluation: a comparison performed by blinded observers. Clin. Gastroenterol. Hepatol. 4(8), 998–1003 (2006)CrossRefGoogle Scholar
  2. 2.
    Chand, N., Mihas, A.A.: Celiac disease: current concepts in diagnosis and treatment. J. Clin. Gastroenterol. 40(1), 3–14 (2006)CrossRefGoogle Scholar
  3. 3.
    Emura, F., Saito, Y.: Narrow-band imaging optical chromocolonoscopy: advantages and limitations. World J. Gastroenterol. 14(31), 4867–4872 (2008)CrossRefGoogle Scholar
  4. 4.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATHGoogle Scholar
  5. 5.
    Fasano, A., Berti, I., Gerarduzzi, T., Not, T., Colletti, R.B., Drago, S., Elitsur, Y., Green, P.H.R., Guandalini, S., Hill, I.D., Pietzak, M., Ventura, A., Thorpe, M., Kryszak, D., Fornaroli, F., Wasserman, S.S., Murray, J.A., Horvath, K.: Prevalence of celiac disease in at-risk and not-at-risk groups in the united states: a large multicenter study. Arch. Intern. Med. 163, 286–292 (2003)CrossRefGoogle Scholar
  6. 6.
    Gadermayr, M., Hegenbart, S., Kwitt, R., Uhl, A.: Narrow band imaging versus white-light: what is best for computer-assisted diagnosis of celiac disease? In: Proceedings of the 13th IEEE International Symposium on Biomedical Imaging (ISBI 2016), pp. 355–359, April 2016Google Scholar
  7. 7.
    Gasbarrini, A., Ojetti, V., Cuoco, L., Cammarota, G., Migneco, A., Armuzzi, A., Pola, P., Gasbarrini, G.: Lack of endoscopic visualization of intestinal villi with the immersion technique in overt atrophic celiac disease. Gastrointest. Endosc. 57, 348–351 (2003)CrossRefGoogle Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: CoRR (2015)Google Scholar
  9. 9.
    Hegenbart, S., Uhl, A., Vécsei, A.: Survey on computer aided decision support for diagnosis of celiac disease. Comput. Biol. Med. 65, 348–358 (2015)CrossRefGoogle Scholar
  10. 10.
    Jiang, J., Trundle, P., Ren, J.: Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 34(8), 617–631 (2010)CrossRefGoogle Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)Google Scholar
  12. 12.
    Niveloni, S., Fiorini, A., Dezi, R., Pedreira, S., Smecuol, E., Vazquez, H., Cabanne, A., Boerr, L.A., Valero, J., Kogan, Z., Maurino, E., Bai, J.C.: Usefulness of videoduodenoscopy and vital dye staining as indicators of mucosal atrophy of celiac disease: assessment of interobserver agreement. Gastrointest. Endosc. 47(3), 223–229 (1998)CrossRefGoogle Scholar
  13. 13.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  14. 14.
    Perronnin, F., Liu, Y., Sanchez, J., Poirier, H.: Large-scale image retrieval with compressed fisher vectors. In: Proceedings of CVPR 2010, pp. 3384–3391 (2010)Google Scholar
  15. 15.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-75690-3_13 CrossRefGoogle Scholar
  16. 16.
    Uhl, A., Vécsei, A., Wimmer, G.: Fractal analysis for the viewpoint invariant classification of celiac disease. In: Proceedings of the 7th International Symposium on Image and Signal Processing (ISPA 2011), Dubrovnik, Croatia, pp. 727–732, September 2011Google Scholar
  17. 17.
    Valitutti, F., Oliva, S., Iorfida, D., Aloi, M., Gatti, S., Trovato, C.M., Montuori, M., Tiberti, A., Cucchiara, S., Di Nardo, G.: Narrow band imaging combined with water immersion technique in the diagnosis of celiac disease. Dig. Liver Dis. 46(12), 1099–1102 (2014)CrossRefGoogle Scholar
  18. 18.
    Vedaldi, A., Lenc, K.: Matconvnet - convolutional neural networks for matlab. In: Proceeding of the ACM International Conference on Multimedia, pp. 689–692 (2015)Google Scholar
  19. 19.
    Yu, J., Chen, J., Xiang, Z.Q., Zou, Y.X.: A hybrid convolutional neural networks with extreme learning machine for wce image classification. In: IEEE International Conference on Robotics and Biomimetics (ROBIO) 2015, pp. 1822–1827, December 2015Google Scholar
  20. 20.
    Zhu, R., Zhang, R., Xue, D.: Lesion detection of endoscopy images based on convolutional neural network features. In: 8th International Congress on Image and Signal Processing (CISP), pp. 372–376, October 2015Google Scholar
  21. 21.
    Zou, Y., Li, L., Wang, Y., Yu, J., Li, Y., Deng, W.J.: Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. IEEE Int. Conf. Digit. Signal Process. (DSP) 2015, 1274–1278 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of SalzburgSalzburgAustria
  2. 2.Department PediatricsSt. Anna Children’s HospitalViennaAustria

Personalised recommendations