Abstract
Image textures are a set of image characteristics used for identifying regions of interests (ROIs) in images. These numerical features can thus be used to classify images in various classifiers. This paper introduces the task of classifying Chest X-ray images with Machine Learning Classifiers and to see the impact of variations on the result of classification. For this purpose, second-order statistical features (GLCM texture features) are extracted from all the images with preprocessing and classification is performed using these features. Various variants are applied for image processing. First-order features are included, the image is divided into multiple regions, different values of distance for GLCM are used. Several evaluation metrics are used to judge the performance of the classifiers. Results on Chest X-ray (Pneumonia) dataset shows remarkable improvements in the accuracy, F1-Score, and the AUC of the classifier.
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Gupta, A., Gupta, A., Verma, V., Khattar, A., Sharma, D. (2020). Texture Feature Extraction: Impact of Variants on Performance of Machine Learning Classifiers: Study on Chest X-Ray – Pneumonia Images. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds) Big Data Analytics. BDA 2020. Lecture Notes in Computer Science(), vol 12581. Springer, Cham. https://doi.org/10.1007/978-3-030-66665-1_11
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