Original title: Prostatic Cells Classification Using Deep Learning
Authors: Majerčík, Jakub ; Špaček, Michal
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Human prostate cancer PC-3 cell line is widely used in cancer research. Previously, Zinc- Resistant variant was described characteristically by higher dry cellular mass determined by quantitative phase imaging. This work aims to classify these 2 cell types into corresponding categories using machine learning methods. We have achieved 97.5% accuracy with the correct preprocessing using Res-Net network.
Keywords: cell classification; deep learning; microscopy; neural network; quantitative phase imaging
Host item entry: Proceedings II of the 26st Conference STUDENT EEICT 2020: Selected papers, ISBN 978-80-214-5868-0

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200663

Permalink: http://www.nusl.cz/ntk/nusl-447715


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