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

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

Abstract

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.

Keywords

Neural networks Image classification Texture features GLCM X-ray Pneumonia 

Notes

Acknowledgements

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.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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