PanNuke: Semi Automatically Generated Tissue Nuclei Instance Classification and Segmentation using Deep Learning Algorithms

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dc.contributor.advisorDr. Mahdy Rahman Chowdhury
dc.contributor.authorFaria Rahman Brishty
dc.contributor.authorUmme Honey Walid Nasha
dc.contributor.id1721419042
dc.contributor.id1512674642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-08-10
dc.date.accessioned2025-08-10T05:50:02Z
dc.date.available2025-08-10T05:50:02Z
dc.date.issued2021-08-30
dc.description.abstractClassification and Segmentation of nuclei instances is one of the challenging task. In the discipline of vision, deep learning has evolved as a branch of the machine learning field. It's a data-processing technique that employs many layers of complicated structures or numerous processing layers made up of various nonlinear transformations. In the branch of medical image data analysis deep learning algorithms are creating benchmarks. Early detection of diseases is important for early treatment. Deep learning has achieved significant advances in computer vision. In this paper, we work on the large PanNuke dataset classification and segmentation. We have obtained outstanding accuracies using deep learning alogrithms.
dc.description.degreeUndergraduate
dc.identifier.cd600000247
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1353
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.titlePanNuke: Semi Automatically Generated Tissue Nuclei Instance Classification and Segmentation using Deep Learning Algorithms
dc.typeProject
oaire.citation.endPage51
oaire.citation.startPage1
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