Analysis of Tuberculosis using Smear Image

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Kanimozhi S
Dr. Nirmala M

Abstract

An automatic method for the detection of Tuberculosis (TB) bacilli from microscopic sputum smear images is presented in this paper. According to WHO, TB is the ninth leading cause of death all over the world. There are various techniques to diagnose TB, of which conventional microscopic sputum smear examination is considered. However, the mentioned method of diagnosis is time intensive and error prone, even in experienced hands. The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a convolutional neural network. We have evaluated our gist algorithm using a dataset of sputum smear microscopic images with different backgrounds (high density and low-density images). Experimental results show that the proposed algorithm achieves for the TB detection. The proposed method automatically detects whether the sputum smear images is infected with TB or not. This method will aid clinicians to predict the disease accurately in a short span of time, thereby helping in improving the clinical outcome.

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