Classification of X-Ray Images for Pneumonia Detection Using Texture Features and Neural Networks

  • Sergio Varela-Santos
  • Patricia MelinEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 862)


Based on the reports of the Center Of Disease Control each year around 50,000 people die because of Pneumonia in the United States, this disease affects the area of the lungs and can be detected (diagnosed) by analyzing chest X-rays. Because of this it’s important the development of computational intelligent techniques for the diagnosis and classification of lung diseases, and as a medical tool for the quick diagnosis of diseases, for this work we used a segment of the ChestXRay14 database which contains radiographic images of several lung diseases including pneumonia, we extracted the area of interest from the pneumonia images using segmentation techniques and furthermore we applied a process of feature extraction on the area of interest of the images to obtain Haralick’s Texture Features and perform classification of the disease using a neural network with good results on the classification of pneumonia X-ray images from healthy X-ray images.


Neural networks Image classification Texture features GLCM X-ray Pneumonia 



we would like to express our gratitude to CONACYT, Tijuana Institute of Technology for the facilities and resources granted for the development of this research.


  1. 1.
    Horváth, G., et al.: A CAD system for screening X-ray Chest radiography. In: Dössel O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering, IFMBE Proceedings, Munich, Germany, vol. 25(5). Springer, Berlin, Heidelberg, 7–12 Sept 2009Google Scholar
  2. 2.
    Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)CrossRefGoogle Scholar
  3. 3.
    Baker, J.A.: Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. Am. J. Roentgenol 181(4), 1083–1088 (2003)CrossRefGoogle Scholar
  4. 4.
    Cheng, J.-Z.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6, 1–13 (2016)CrossRefGoogle Scholar
  5. 5.
    Miranda, E., Aryuni, M., Irwansyah, E.: A survey of medical image classification techniques. In: 2016 International Conference on Information Management and Technology (ICIMTech), pp. 56–61, IEEE, Bandung, Indonesia (2016)Google Scholar
  6. 6.
    Van Ginneken, B., Ter Haar Romeny, B.M., Viergever, M.A.: Computer-aided diagnosis in chest radiography: a survey. IEEE Trans. Med. Imaging 20(12), 1228–1241 (2001)CrossRefGoogle Scholar
  7. 7.
    Rubio, Y., Montiel, O., Sepúlveda, R.: Microcalcification detection in mammograms based on fuzzy logic and cellular automata. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol. 667. Springer, Cham (2017)Google Scholar
  8. 8.
    González, B., Valdez, F., Melin, P., Prado-Arechiga, G.: Echocardiogram image recognition using neural networks. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds.) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol. 547. Springer, Cham (2014)Google Scholar
  9. 9.
    González, B., Melin, P., Valdez, F., Prado-Arechiga, G.: Ensemble neural network optimization using a gravitational search algorithm with interval type-1 and type-2 fuzzy parameter adaptation in pattern recognition applications. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham (2018)Google Scholar
  10. 10.
    González, B., Valdez, F., Melin, P., Prado-Arechiga, G.: A gravitational search algorithm for optimization of modular neural networks in pattern recognition. In: Castillo, O., Melin, P. (eds.) Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics. Studies in Computational Intelligence, vol. 574. Springer, Cham (2015)Google Scholar
  11. 11.
    Pneumonia – National Center for Health Statistics – Center for Disease Control and Prevention,, last accessed 2018/09/03
  12. 12.
    Pérez-Padilla, J.R.: Muertes respiratorias en México 2015. Neumol Cir Torax 77(3), 198–202 (2018)Google Scholar
  13. 13.
    Pneumonia—World Health Organization. Last accessed 05 November 2018
  14. 14.
    Pneumonia—Radiological Society of North America. Last accessed 07 October 2019
  15. 15.
    Duong, M., Timoney, P., MacNicholas, R., Kitchen, J., Mustapha, M., Arunasalam, U.: ABC’s of chest X-rays”. Trinity Stud. Med. J. 2 (2011)Google Scholar
  16. 16.
    Pneumonia—University Of Virginia—Pneumonia Pathology. Last accessed 07 October 2019
  17. 17.
    Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chest X-ray 8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  18. 18.
    Li, J., Xu, Z., Zhang, Y.: Diagnosing chest x-ray diseases with deep learningGoogle Scholar
  19. 19.
    Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M., Ng, A.Y.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning (2017)Google Scholar
  20. 20.
    Antin, B., Kravitz, J., Martayan, E.: Detecting pneumonia in chest X-rays with supervised learningGoogle Scholar
  21. 21.
    Khobragade, S., Tiwari, A., Patil, C.Y., Narke, V.: Automatic detection of major lung diseases using chest radiographs and classification by feed-forward artificial neural network. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE, Delhi, India (2016)Google Scholar
  22. 22.
    Pavithra, R., Pattar, S.Y.: Detection and classification of lung disease—pneumonia and lung cancer in chest radiology using artificial neural networks. Int. J. Sci. Res. Publ. 5(10), 1–5 (2015)Google Scholar
  23. 23.
    Bankman, I.N.: Handbook of Medical Image Processing and Analysis, 2nd edn. Academic Press, San Diego, CA, USA (2008)Google Scholar
  24. 24.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. Vol. 1, 1st edn. Addison-Wesley Publishing Company, Inc, USA (1992)Google Scholar
  25. 25.
    Haralick, R.M.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973)CrossRefGoogle Scholar
  26. 26.
    Tuceryan, M., Jain, A.K.: Texture analysis. Handbook of Pattern Recognition and Computer Vision. 1st edn. World Scientific Publishing, Singapore (1993)CrossRefGoogle Scholar
  27. 27.
    Karthikeyan, S.: Performance analysis of gray level co-occurrence matrix texture features for glaucoma diagnosis. Am. J. Appl. Sci. 11(2), 248–257 (2014)CrossRefGoogle Scholar
  28. 28.
    Melin, P., González, C.I., Castro, J.R., Mendoza, O., Castillo, O.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)CrossRefGoogle Scholar
  29. 29.
    González, C.I., Melin, P., Castro, J.R., Castillo, O., Mendoza, O.: Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)CrossRefGoogle Scholar
  30. 30.
    González, C.I., Melin, P., Castro, J.R., Mendoza, O., Castillo, O.: An improved Sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)CrossRefGoogle Scholar
  31. 31.
    Ontiveros, E., Melin, P., Castillo, O.: High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems. Eng. Appl. AI 74, 186–197 (2018)CrossRefGoogle Scholar
  32. 32.
    Melin, P., Sánchez, D., Castillo, O.: Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)CrossRefGoogle Scholar
  33. 33.
    Melin, P., Sánchez, D.: Multi-objective optimization for modular granular neural networks applied to pattern recognition. Inf. Sci. 460–461, 594–610 (2018)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Sánchez, D., Melin, P., Castillo, O.: Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. of AI 64, 172–186 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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