Abnormality Detection of TB using Instance Learned Classifier on Lung CT Images
P. Prasanna Kumari1, B. Prabhakara Rao2

1P. Prasanna Kumari, Research Scholar, Department of ECE, JNTUK, Kakinada (A.P), India.
2Dr. B. Prabhakara Rao, Program Director, Department of Nanotechnology, IST, JNTUK, Kakinada (A.P), India.
Manuscript received on 13 October 2019 | Revised Manuscript received on 22 October 2019 | Manuscript Published on 02 November 2019 | PP: 979-982 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11630982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1163.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Tuberculosis is one of the serious health threatening disease which is more ubiquitous in African countries. According to World Health Organization[1], it is the second leading cause of death from an infection after HIV and 10.4 million cases of TB and 1.3 million deaths were reported worldwide in 2017 based on the estimates of Centre for Disease Control and Prevention (CDC). This paper presents abnormality detection of TB using an instance based learning classifier known as the KNN classifier an SVM classifier. If input image classified as abnormal then abnormal region is extracted using the segmentation. The system has been tested on the number of lung CT scan images.
Keywords: Support Vector Machine (SVM), K–Nearest Neighbor (KNN), Computed Tomography (CT), Tuberculosis(TB).
Scope of the Article: Image Security