Front Inner Page - Volume 1 No.3 December 2014

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  • Title

    :

    Accurate TB Manifestation using Multi Class SVM Classifier

    Authors

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    Mr.P.JohnVivek1, Swathika.S.R2

    Keywords

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    CAD and diagnosis, lung nodule, pattern recognition and classification, segmentation, tuberculosis (TB), X-ray imaging. Multiclass SVM classifier.

    Issue Date

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    December – 2014

    Abstract

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    TB is one of the leading cause of death worldwide, with a mortality rate of over 1.2 million people in [2010].When TB is left undiagnosed, mortality rates will be high. This paper presents an accurate approach for detecting TB using a well-known classifier known as the Multiclass SVM classifier. In this paper, we first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enables the X-rays to be classify the lung region  as normal, moderate or severe(TB affected) using a Multi-class SVM Classifier. In an effort to reduce the burden of TB, this recent approach achieves a maximum accuracy in identifying TB. This proposed system for TB manifestation achieves an accuracy of 94.3% compared with the earlier methods [1] which achieves an accuracy of 86%.We collect the dataset from SKS hospital and perform the classification for the received dataset. We compare the performance of the received dataset with the classifiers: KNN, SVM & Multi-class SVM classifier. Among the classifiers, the Multiclass SVM Classifier achieves a maximum accuracy. Hence the Multi-class SVM classifier is promising in achieving the maximum performance up to the human experts.

    Page(s)

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    1-8

    ISSN

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    2347- 4734

    Source

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    Vol. 1, No.3, December 2014

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