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Fractional crow search-based support vector neural network for patient classification and severity analysis of tuberculosis

Fractional crow search-based support vector neural network for patient classification and severity analysis of tuberculosis

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The world is chasing towards the automation in the severity analysis and classification of the patients based on the severity of tuberculosis (TB). The automatic classification is very much useful for developing countries that are struggling to reduce the fatality rate of the persons suffering from TB as it is a top standing infectious disease. Thus, the automatic classification of the TB patients using the sputum images with the proposed fractional crow search-based support vector neural network is presented. The proposed classification method is the integration of the fractional theory in the crow search algorithm that increases the computational speed and reduces the cost and time spent on analysing the test samples. The importance of the proposed method is that it requires minimum manual power and hence, the inaccuracies are reduced. The experimentation performed using the Ziehl–Neelsen sputum smear microscopy image database proves that the proposed classifier is highly accurate and offered an improved performance in terms of accuracy rate, true positive rate, and false-positive rate.

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