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)

SDG 3 Good Health and Well-being

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