Metastatic cancer detection using machine learning

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dc.contributor.authorChaya, Ravindra
dc.contributor.authorChevula, Srinivasulu (16ET13)
dc.contributor.authorKhan, Akbar Aslam (16ET19)
dc.contributor.authorShaikh, Anwari Jahan (17DET35)
dc.contributor.authorAnsari, Fahim Ahemd (16ET09)
dc.date.accessioned2021-11-09T06:40:30Z
dc.date.available2021-11-09T06:40:30Z
dc.date.issued2020-05
dc.description.abstractWe propose a model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysis showing improved stability on predictions, and demonstrate that exploiting rotation equivariance significantly improves tumor detection performance on a challenging lymph node metastases dataset. We further present a novel derived dataset to enable principled comparison of machine learning models. We want to achieve 97% accuracy by using CCN algorithm in machine learning.en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/3624
dc.language.isoenen_US
dc.publisherAIKTCen_US
dc.subjectProject Report - EXTCen_US
dc.titleMetastatic cancer detection using machine learningen_US
dc.typeOtheren_US
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