 Chronological white shark optimisation_PyramidNet for tuberculosis bacilli segmentation and infection level identificationGavendra Singh and Faizur Rashid International Journal of Industrial and Systems Engineering, 2026, vol. 53, issue 2, 188-214 Abstract: The main objective of this research is to identify the infection level or severity of tuberculosis (TB) by PyramidNet which is trained by chronological white shark optimisation (CWSO). TB is usually caused by mycobacterium TB bacteria, which generally affects the lungs. This also affects other body parts. Most infections of TB show no symptoms, which is known as latent TB Bacteria causing TB are spread while the infected person sneezes or coughs. Weight loss, night sweats, as well as fever are common symptoms of TB. In this work, finding the severity of TB or its infection level is done by DL enabled optimised algorithm. Here, PyramidNet is a DL model, trained by the proposed CWSO for infection level identification. Also, denoising is carried by the median filter in the pre-processing stage and bacilli segmentation is done by SegNet, trained by the proposed CWSO. Moreover, appropriate features extracted are fed for severity identification of TB by PyramidNet. Furthermore, CWSO is the hybridisation of chronological concept along with WSO. Keywords: tuberculosis; TB; white shark optimisation; WSO; PyramidNet; SegNet; median filter. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:53:y:2026:i:2:p:188-214 Access Statistics for this article More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd |
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