tuberculosis
Any damage that affects the normal functioning of the lungs is termed as a lung disease,
which can prove fatal if not detected early. To address this challenge, two innovative techniques proposed
for the lung disease classification, supporting medical professionals to diagnose and provides preventive
measures at an early stage. The proposed Model 1 integrates a custom MobileNetV2L2 architecture, that
builds upon the MobileNetV2 framework through fine-tuning and customization. This model incorporates a
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Tuberculosis (TB) remains a major global health problem with high incidence and mortality rates worldwide. In recent years, with the rapid development of computer-aided diagnosis (CAD) tools, CAD has played an increasingly important role in supporting tuberculosis diagnosis. However, the development of CAD for TB diagnosis relies heavily on well-annotated computerized tomography (CT) datasets. Unfortunately, the currently available annotations in TB CT datasets are still limited, which hinders the development of CAD tools for TB diagnosis to some extent.
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Tuberculosis (TB) remains a major global health problem with high incidence and mortality rates worldwide. In recent years, with the rapid development of computer-aided diagnosis (CAD) tools, CAD has played an increasingly important role in supporting tuberculosis diagnosis. However, the development of CAD for TB diagnosis relies heavily on well-annotated computerized tomography (CT) datasets. Unfortunately, the currently available annotations in TB CT datasets are still limited, which hinders the development of CAD tools for TB diagnosis to some extent.
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In order to contribute to the development of automatic methods for the detection of bacilli, TBimages is an image dataset composed of two subsets: TbImages_SS1 contains 10 images per field, of different focal depths, and aims to support the definition of autofocus metrics and also the development of extended focus imaging methods to facilitate the detection of bacilli in smear microscopy imaging. TbImages_SS2 aims to support the development of automatic bacilli detection.
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