Automatic Classification of Cancer Cells using Digital Holography

Seonghwan Park; Jaewon Jeong; Inkyu Moon

Automatic Classification of Cancer Cells using Digital Holography

Seonghwan Park, Jaewon Jeong, Inkyu Moon

Department of Robotics and Mechatronics Engineering

Daegu Gyeongbuk Institute of Science and Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper overviews the feature-based machine learning and image-based deep learning techniques to automatically classify elliptical cancer cells at the single-cell level by using marker-free digital holographic imaging techniques.

Original language	English
State	Published - 2023
Event	2023 Novel Optical Materials and Applications, NOMA 2023 - Busan, Korea, Republic of Duration: 9 Jul 2023 → 13 Jul 2023

Conference

Conference	2023 Novel Optical Materials and Applications, NOMA 2023
Country/Territory	Korea, Republic of
City	Busan
Period	9/07/23 → 13/07/23

Bibliographical note

Publisher Copyright:
© 2023 The Author(s).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@conference{293df500edf9426d99578a830ff91c4b,

title = "Automatic Classification of Cancer Cells using Digital Holography",

abstract = "This paper overviews the feature-based machine learning and image-based deep learning techniques to automatically classify elliptical cancer cells at the single-cell level by using marker-free digital holographic imaging techniques.",

author = "Seonghwan Park and Jaewon Jeong and Inkyu Moon",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s).; 2023 Novel Optical Materials and Applications, NOMA 2023 ; Conference date: 09-07-2023 Through 13-07-2023",

year = "2023",

language = "English",

}

TY - CONF

T1 - Automatic Classification of Cancer Cells using Digital Holography

AU - Park, Seonghwan

AU - Jeong, Jaewon

AU - Moon, Inkyu

PY - 2023

Y1 - 2023

N2 - This paper overviews the feature-based machine learning and image-based deep learning techniques to automatically classify elliptical cancer cells at the single-cell level by using marker-free digital holographic imaging techniques.

AB - This paper overviews the feature-based machine learning and image-based deep learning techniques to automatically classify elliptical cancer cells at the single-cell level by using marker-free digital holographic imaging techniques.

UR - https://www.scopus.com/pages/publications/85215503331

M3 - Paper

AN - SCOPUS:85215503331

T2 - 2023 Novel Optical Materials and Applications, NOMA 2023

Y2 - 9 July 2023 through 13 July 2023

ER -

Automatic Classification of Cancer Cells using Digital Holography

Abstract

Conference

Bibliographical note

UN SDGs

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Cite this