Detection of ZN stained mycobacterium tuberculosis in formalin fixed paraffin embedded tissue through deep learning

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dc.contributor.authorAmina Akhtar
dc.contributor.authorUzair Khawaja
dc.contributor.authorAli Zaib Rana
dc.date.accessioned2025-12-21T18:08:10Z
dc.date.available2025-12-21T18:08:10Z
dc.date.issued2022
dc.description.abstractFast detection of tuberculosis helps in diagnosis of the disease so that the subject may receive better treatment to be safe from further complications. While working with the conventional methods, we came to see that it requires more time and results in delay of the outcome, which can cause further complications in the diseased person. The main focus is to study the pathological section, so the disease is studied at microscopic level. We use the approach of formalin fixed paraffin embedded (FFPE) tissue through deep learning so that the samples may be detected via some image sensor, which will further compare our samples with the datasets provided for the following disease. A new technique for diagnosing tuberculosis relies on high-sensitivity microscopy. We offer a new TB diagnosis strategy based on deep learning for bacillus recognition from sputum microscopy pictures. The suggested approach takes a sputum microscopy picture with the appropriate zoom level used as an input, and the locations of suspected Mycobacterium TB bacilli are output. Our method's great sensitivity, when demonstrated at the level, does have the potential to become an effective and reliable TB detection imaging technique.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/17542
dc.language.isoen
dc.publisherUMT Lahore
dc.titleDetection of ZN stained mycobacterium tuberculosis in formalin fixed paraffin embedded tissue through deep learning
dc.typeThesis
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