Abstract
Tuberculosis is a highly infectious disease and the second largest killer worldwide. Every year, millions of new cases and deaths are reported due to tuberculosis. In the developing countries, tuberculosis suspect cases are enormous, and hence, a large number of radiologists are required to perform the mass screening. Therefore attempts have been made to design computer-aided diagnosis (CAD) systems for automatic mass screening. A CAD system generally consists of four phases, namely, preprocessing, segmentation, feature extraction, and classification. In this paper, we present a survey of the recent approaches used in different phases of chest radiographic CAD system for tuberculosis detection.
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Hooda, R., Mittal, A., Sofat, S. (2019). A Survey of CAD Methods for Tuberculosis Detection in Chest Radiographs. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_25
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