Abstract
Lung cancer is a most common disease nowadays, so to get rid of it we have made a detection system. In this paper, an active spline model is used to segment the X-ray images of lung cancer. The system formed acquired medical images of lung X-ray. First, in preprocessing median filter is used for noise detection. Then, segmentation is applied and further K-mean and fuzzy C-mean clustering is applied for feature extraction. This paper is an extension of techniques of image processing of lung cancer detection and produces the final results of feature extraction after X-ray image segmentation. Here, the proposed model is developed using SVM algorithm used for classification. Using MATLAB, simulation results are obtained for cancer detection system. This paper focuses thus on segmentation and detection of lung cancer by finding normality and abnormality of the images.
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Acknowledgements
Initially, we would like to thank our almighty in the success of completing this work. We want to thank the RJ Superspecialities Hospital & Heart Center for supporting and providing large collection of lung X-ray images, which have been valuable for this research.
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Joon, P., Bajaj, S.B., Jatain, A. (2019). Segmentation and Detection of Lung Cancer Using Image Processing and Clustering Techniques. In: Pati, B., Panigrahi, C., Misra, S., Pujari, A., Bakshi, S. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-13-1708-8_2
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DOI: https://doi.org/10.1007/978-981-13-1708-8_2
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