Abstract
In this study, the problem of detecting pneumonia in chest x-ray images is addressed. The method used is based on the computation of locally adaptive regression kernel (LARK) descriptors, which, when used in conjunction with the novel Support Vector Machine (SVM) classifier, obtained a 98% precision and 98% recall tested in only 400 images and a 96% precision and 95% recall tested in 1000 images in a chest x-ray images dataset. Different sample sizes were also tested to show that the method is robust even with a small sample size. This method was also shown to obtain better performance compared to two other classifiers, namely Random Forest (RF) and decision trees (DT).
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References
Alaniz II, A.L., Mantaring, C.M.G.: Using local steering kernels to detect people in videos
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Chikhalekar, A.: Analysis of image processing for digital x-ray. Int. Res. J. Eng. Technol. (IRJET) e-ISSN 2395–0056 (2016)
Correa, M., et al.: Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition. PLoS One 13(12) (2018). https://doi.org/10.1371/journal.pone.0206410
Deng, G., Cahill, L.: An adaptive Gaussian filter for noise reduction and edge detection. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, pp. 1615–1619. IEEE (1993)
Gu, X., Pan, L., Liang, H., Yang, R.: Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. In: Proceedings of the 3rd International Conference on Multimedia and Image Processing, pp. 88–93 (2018). https://doi.org/10.1145/3195588.3195597. https://dl.acm.org/citation.cfm?id=3195597
Jolliffe, I.: Principal component analysis. In: International Encyclopedia of Statistical Science, pp. 1094–1096. Springer, Heidelberg (2011)
Maini, R., Aggarwal, H.: A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053 (2010)
Mohanty, N., John, A.L.S., Manmatha, R., Rath, T.: Shape-based image classification and retrieval, vol. 31. Elsevier (2013). https://doi.org/10.1016/B978-0-444-53859-8.00010-2
Mooney, P.: Chest x-ray images (pneumonia), March 2018. https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/version/2
Nisbet, R., Miner, G., Yale, K.: Advanced algorithms for data mining handbook of statistical analysis and data mining applications (2018). https://doi.org/10.1016/B978-0-12-416632-5.00008-6
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Yu, P., Xu, H., Zhu, Y., Yang, C., Sun, X., Zhao, J.: An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs. J. Digit. Imaging 24(3), 382–393 (2011)
Qin, C., Yao, D., Shi, Y., Song, Z.: Computer-aided detection in chest radiography based on artificial intelligence: a survey. BioMed. Eng. OnLine 17(1), 113 (2018). https://doi.org/10.1186/s12938-018-0544-y
Rajaraman, S., Candemir, S., Kim, I., Thoma, G., Antani, S.: Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl. Sci. 8(10) (2018). https://doi.org/10.3390/app8101715. https://www.mdpi.com/2076-3417/8/10/1715
Russ, J.C.: The Image Processing Handbook. CRC Press, Boca Raton (2016)
Seo, H.J., Milanfar, P.: Face verification using the lark representation. IEEE Trans. Inf. Forensics Secur. 6(4), 1275–1286 (2011)
Wang, J., Neskovic, P., Cooper, L.N.: Training data selection for support vector machines. In: ICNC 2005: Advances in Natural Computation, pp. 554–564 (2005)
World Health Organization: Pneumonia (2019). https://www.who.int/news-room/fact-sheets/detail/pneumonia
Xu, X., Xu, S., Jin, L., Song, E.: Characteristic analysis of otsu threshold and its applications. Pattern Recogn. Lett. 32(7), 956–961 (2011)
Zhou, H., Wei, L., Lim, C.P., Creighton, D., Nahavandi, S.: Robust vehicle detection in aerial images using bag-of-words and orientation aware scanning. IEEE Trans. Geosci. Remote Sens. 99, 1–12 (2018)
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Ambita, A.A.E., Boquio, E.N.V., Naval, P.C. (2020). Locally Adaptive Regression Kernels and Support Vector Machines for the Detection of Pneumonia in Chest X-Ray Images. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_11
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