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.
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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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Varela-Santos, S., Melin, P. (2020). Classification of X-Ray Images for Pneumonia Detection Using Texture Features and Neural Networks. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_20
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DOI: https://doi.org/10.1007/978-3-030-35445-9_20
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