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2020 | OriginalPaper | Chapter

Texture Feature Extraction: Impact of Variants on Performance of Machine Learning Classifiers: Study on Chest X-Ray – Pneumonia Images

Authors : Anamika Gupta, Anshuman Gupta, Vaishnavi Verma, Aayush Khattar, Devansh Sharma

Published in: Big Data Analytics

Publisher: Springer International Publishing

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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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Agarwal, C., Sharma, A.: Image understanding using decision tree based machine learning. In: ICIMU 2011: Proceedings of the 5th International Conference on Information Technology & Multimedia, pp. 1–8. IEEE, Kuala Lumpur (2011). https://doi.org/10.1109/icimu.2011.6122757

Aggarwal, N., Agrawal, R.K.: First and second order statistics features for classification of magnetic resonance brain images. J. Sig. Inf. Process. 3(2), 146–153 (2012). https://doi.org/10.4236/jsip.2012.32019CrossRef

Ankita, R., Kumari, C.U., Mehdi, M.J., Tejashwini, N., Pavani, T.: Lung cancer image-feature extraction and classification using GLCM and SVM classifier. Int. J. Innov. Technol. Explor. Eng. 8(11), 2211–2215 (2019). https://doi.org/10.35940/ijitee.K2044.0981119CrossRef

Çelik, M., Dadaşer-Çelik, F., Dokuz, A.Ş.: Anomaly detection in temperature data using DBSCAN algorithm. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications, pp. 91–95. IEEE, Istanbul (2011). https://doi.org/10.1109/inista.2011.5946052

Chandra, T.B., Verma, K., Singh, B.K., Jain, D., Netam, S.S.: Automatic detection of tuberculosis related abnormalities in chest x-ray images using hierarchical feature extraction scheme. Expert Syst. Appl. 158, 113514 (2020). https://doi.org/10.1016/j.eswa.2020.113514CrossRef

Chatterjee, S., Dey, D., Munshi, S.: Integration of morphological preprocessing and fractal-based feature extraction with recursive feature elimination for skin lesion types classification. Comput. Methods Programs Biomed. 178, 201–218 (2019). https://doi.org/10.1016/j.cmpb.2019.06.018CrossRef

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953CrossRefMATH

Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification purposes. Image Vis. Comput. 19(9–10), 699–707 (2001). https://doi.org/10.1016/S0262-8856(01)00045-2CrossRef

Gindi, A., Attiatalla, T.A., Sami, M.M.: A comparative study for comparing two feature extraction methods and two classifiers in classification of early stage lung cancer diagnosis of chest x-ray images. J. Am. Sci. 10(6), 13–22 (2014). https://doi.org/10.7537/marsjas100614.03CrossRef

10.

Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314CrossRef

11.

Kaggle, Chest X-Ray Images (Pneumonia) Dataset Page. https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed 13 Sept 2020

12.

Kim, J.I.N.H.O., Kim, B.S., Savarese, S.: Comparing image classification methods: K-nearest-neighbor and support-vector-machines. In: Proceedings of the 6th WSEAS International Conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, vol. 1001, pp. 133–138. WSEAS, Wisconsin (2012)

13.

Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E.: Data preprocessing for supervised learning. Int. J. Comput. Sci. 1(2), 111–117 (2006)

14.

Mall, P.K., Singh, P.K., Yadav, D.: GLCM based feature extraction and medical X-RAY image classification using machine learning techniques. In: 2019 IEEE Conference on Information and Communication Technology, pp. 1–6. IEEE, Allahabad (2019). https://doi.org/10.1109/cict48419.2019.9066263

15.

NHLBI Website, Pneumonia. https://www.nhlbi.nih.gov/health-topics/pneumonia. Accessed 13 Sept 2020

16.

Pugalenthi, R., Rajakumar, M.P., Ramya, J., Rajinikanth, V.: Evaluation and classification of the brain tumor MRI using machine learning technique. J. Control Eng. Appl. Inform. 21(4), 12–21 (2019)

17.

Rish, I.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, pp. 41–46 (2001)

18.

Sunasra, M.: Performance Metrics for Classification problems in Machine Learning. https://medium.com/@MohammedS/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b. Accessed 15 Sept 2020

19.

World Health Organization (WHO), Pneumonia facts. https://www.who.int/news-room/fact-sheets/detail/pneumonia. Accessed 13 Sept 2020

Title: Texture Feature Extraction: Impact of Variants on Performance of Machine Learning Classifiers: Study on Chest X-Ray – Pneumonia Images
Authors: Anamika Gupta
Anshuman Gupta
Vaishnavi Verma
Aayush Khattar
Devansh Sharma
Publisher: Springer International Publishing
Book: Big Data Analytics
Print ISBN: 978-3-030-66664-4

Electronic ISBN: 978-3-030-66665-1

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-66665-1_11

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