Evaluation of Deep Feedforward Neural Networks for Classification of Diffuse Lung Diseases

  • Isadora Cardoso
  • Eliana Almeida
  • Héctor Allende-CidEmail author
  • Alejandro C. Frery
  • Rangaraj M. Rangayyan
  • Paulo M. Azevedo-Marques
  • Heitor S. Ramos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


Diffuse Lung Diseases (DLDs) are a challenge for physicians due their wide variety. Computer-Aided Diagnosis (CAD) are systems able to help physicians in their diagnoses combining information provided by experts with Machine Learning (ML) methods. Among ML techniques, Deep Learning has recently established itself as one of the preferred methods with state-of-the-art performance in several fields. In this paper, we analyze the discriminatory power of Deep Feedforward Neural Networks (DFNN) when applied to DLDs. We classify six radiographic patterns related with DLDs: pulmonary consolidation, emphysematous areas, septal thickening, honeycomb, ground-glass opacities, and normal lung tissues. We analyze DFNN and other ML methods to compare their performance. The obtained results show the high performance obtained by DFNN method, with an overall accuracy of 99.60%, about 10% higher than the other studied ML methods.


Computer-Aided Diagnosis Deep Feedforward Neural Network Deep Learning Diffuse Lung Diseases Machine Learning 



This work was partially funded by Fapeal, CNPq, and SEFAZ-AL. The work of Héctor Allende-Cid was supported by the project FONDECYT Initiation into Research 11150248.


  1. 1.
    Almeida, E., Rangayyan, R., Azevedo-Marques, P.: Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC, pp. 719–722 (2015)Google Scholar
  2. 2.
    Almeida, E., Rangayyan, R., Azevedo-Marques, P.: Gaussian mixture modeling for statistical analysis of features of high-resolution CT images of diffuse pulmonary diseases. In: 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5 (2015)Google Scholar
  3. 3.
    Almiron, M., Almeida, E., Miranda, M.: The reliability of statistical functions in four software packages freely used in numerical computation. Braz. J. Probab. Stat. 23(2), 107–119 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)zbMATHGoogle Scholar
  5. 5.
    Cai, Z., Xu, D., Zhang, Q., Zhang, J., Ngai, S., Shao, J.: Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol. BioSyst. 11, 791–800 (2015)CrossRefGoogle Scholar
  6. 6.
    Candel, A., Parmar, V., LeDell, E., Arora, A.: Deep Learning with H2O, 5th edn. Inc, Mountain View (2017)Google Scholar
  7. 7.
    Fraley, C., Raftery, A.: Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc. 97, 611–631 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Society for Artificial Intelligence and Statistics (2010)Google Scholar
  9. 9.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  10. 10.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)CrossRefGoogle Scholar
  11. 11.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009). CrossRefzbMATHGoogle Scholar
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  13. 13.
    Laws, K.: Rapid texture identification. In: Proceedings of SPIE Conference on Image Processing for Missile Guidance, vol. 238, pp. 376–380 (1980)Google Scholar
  14. 14.
    Litjens, G., Kooi, T., Bejnordi, B., Setio, A., Ciompi, F., Ghafoorian, M., Laak, J., Ginneken, B., Sánchez, C.: A survey on deep learning in medical image analysis. CoRR abs/1702.05747 (2017)Google Scholar
  15. 15.
    McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Hoboken (2004)zbMATHGoogle Scholar
  16. 16.
    Pereyra, L., Rangayyan, R., Ponciano-Silva, M., Azevedo-Marques, P.: Fractal analysis for computer-aided diagnosis of diffuse pulmonary diseases in HRCT images. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6 (2014)Google Scholar
  17. 17.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2016).
  18. 18.
    Rangayyan, R.: Biomedical Image Analysis. CRC Press, Boca Raton (2004)CrossRefGoogle Scholar
  19. 19.
    Ravanelli, M., Brakel, P., Omologo, M., Bengio, Y.: A network of deep neural networks for distant speech recognition. CoRR abs/1703.08002 (2017)Google Scholar
  20. 20.
    Ripley, B., Hjort, N.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1995)Google Scholar
  21. 21.
    Rubin, A., Santana, A., Costa, A., Baldi, B., Pereira, C., et al.: Diretrizes de doenças pulmonares intersticiais da Sociedade Brasileira de Pneumologia e Tisiologia. Braz. J. Pulmonol. 38(suppl. 2), S1–S133 (2012)Google Scholar
  22. 22.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  23. 23.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)Google Scholar
  24. 24.
    Zaki, M., Meira Jr., W.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014)zbMATHGoogle Scholar
  25. 25.
    Zeiler, M.D.: ADADELTA: An adaptive learning rate method. CoRR abs/1212.5701 (2012)Google Scholar
  26. 26.
    Zheng, B., Yoon, S., Lam, S.S.: Breast cancer diagnosis based on feature extraction using a hybrid of k-means and support vector machine algorithms. Expert Syst. Appl. 41(4), 1476–1482 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto de ComputaçãoUniversidade Federal de AlagoasMaceióBrazil
  2. 2.Escuela de Ingeniería InformáticaPontificia Universidad Católica de ValparaísoValparaísoChile
  3. 3.Department of Electrical and Computer Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  4. 4.Department of Internal Medicine, Ribeirão Preto Medical SchoolUniversity of São PauloRibeirão PretoBrazil

Personalised recommendations