Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality

Szu Yin Lin; Ming Chun Chien; Edwin Tiong Kwong Meng; Yu Chien Wang; Yu Yi Kuo; Che Hsuan Lin

doi:10.1109/APSIPAASC58517.2023.10317187

Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality

Szu Yin Lin^*, Ming Chun Chien, Edwin Tiong Kwong Meng, Yu Chien Wang, Yu Yi Kuo, Che Hsuan Lin

^*此作品的通信作者

管理科學系

研究成果: Conference contribution › 同行評審

摘要

According to the World Health Organization (WHO), in 2022, breast cancer is the most diagnosed cancer among women worldwide, irrespective of age. In Taiwan, it is the most prevalent cancer among women and has the fourth-highest mortality rate. Additionally, diagnosing breast cancer often takes a long time searching for symptoms. In this study, we propose utilizing artificial intelligence image analysis methods and mixed reality interfaces to track and compare breast cancer images. We employed a deep learning convolutional neural network to analyze and classify acquired images of benign or malignant cases. Thus, establishing breast cancer tracking and a diagnostic decision support system for physicians to reference would benefit the future medical field. By incorporating mixed reality technology, doctors can save time and reduce labor costs without being constrained by geographic limitations.

原文	English
主出版物標題	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	1903-1906
頁數	4
ISBN（電子）	9798350300673
DOIs	https://doi.org/10.1109/APSIPAASC58517.2023.10317187
出版狀態	Published - 2023
事件	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, 台灣持續時間: 31 10月 2023 → 3 11月 2023

出版系列

名字	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
國家/地區	台灣
城市	Taipei
期間	31/10/23 → 3/11/23

UN SDG

此研究成果有助於以下永續發展目標

存取文件

10.1109/APSIPAASC58517.2023.10317187

其它文件與鏈接

Link to publication in Scopus

引用此

Lin, S. Y., Chien, M. C., Meng, E. T. K., Wang, Y. C., Kuo, Y. Y., & Lin, C. H. (2023). Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality. 於 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 (頁 1903-1906). (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC58517.2023.10317187

Lin, Szu Yin ; Chien, Ming Chun ; Meng, Edwin Tiong Kwong 等. / Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 頁 1903-1906 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

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title = "Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality",

abstract = "According to the World Health Organization (WHO), in 2022, breast cancer is the most diagnosed cancer among women worldwide, irrespective of age. In Taiwan, it is the most prevalent cancer among women and has the fourth-highest mortality rate. Additionally, diagnosing breast cancer often takes a long time searching for symptoms. In this study, we propose utilizing artificial intelligence image analysis methods and mixed reality interfaces to track and compare breast cancer images. We employed a deep learning convolutional neural network to analyze and classify acquired images of benign or malignant cases. Thus, establishing breast cancer tracking and a diagnostic decision support system for physicians to reference would benefit the future medical field. By incorporating mixed reality technology, doctors can save time and reduce labor costs without being constrained by geographic limitations.",

author = "Lin, {Szu Yin} and Chien, {Ming Chun} and Meng, {Edwin Tiong Kwong} and Wang, {Yu Chien} and Kuo, {Yu Yi} and Lin, {Che Hsuan}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 ; Conference date: 31-10-2023 Through 03-11-2023",

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doi = "10.1109/APSIPAASC58517.2023.10317187",

language = "English",

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Lin, SY, Chien, MC, Meng, ETK, Wang, YC, Kuo, YY & Lin, CH 2023, Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality. 於 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Institute of Electrical and Electronics Engineers Inc., 頁 1903-1906, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Taipei, 台灣, 31/10/23. https://doi.org/10.1109/APSIPAASC58517.2023.10317187

Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality. / Lin, Szu Yin; Chien, Ming Chun; Meng, Edwin Tiong Kwong 等.
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1903-1906 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality

AU - Lin, Szu Yin

AU - Chien, Ming Chun

AU - Meng, Edwin Tiong Kwong

AU - Wang, Yu Chien

AU - Kuo, Yu Yi

AU - Lin, Che Hsuan

PY - 2023

Y1 - 2023

N2 - According to the World Health Organization (WHO), in 2022, breast cancer is the most diagnosed cancer among women worldwide, irrespective of age. In Taiwan, it is the most prevalent cancer among women and has the fourth-highest mortality rate. Additionally, diagnosing breast cancer often takes a long time searching for symptoms. In this study, we propose utilizing artificial intelligence image analysis methods and mixed reality interfaces to track and compare breast cancer images. We employed a deep learning convolutional neural network to analyze and classify acquired images of benign or malignant cases. Thus, establishing breast cancer tracking and a diagnostic decision support system for physicians to reference would benefit the future medical field. By incorporating mixed reality technology, doctors can save time and reduce labor costs without being constrained by geographic limitations.

AB - According to the World Health Organization (WHO), in 2022, breast cancer is the most diagnosed cancer among women worldwide, irrespective of age. In Taiwan, it is the most prevalent cancer among women and has the fourth-highest mortality rate. Additionally, diagnosing breast cancer often takes a long time searching for symptoms. In this study, we propose utilizing artificial intelligence image analysis methods and mixed reality interfaces to track and compare breast cancer images. We employed a deep learning convolutional neural network to analyze and classify acquired images of benign or malignant cases. Thus, establishing breast cancer tracking and a diagnostic decision support system for physicians to reference would benefit the future medical field. By incorporating mixed reality technology, doctors can save time and reduce labor costs without being constrained by geographic limitations.

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DO - 10.1109/APSIPAASC58517.2023.10317187

M3 - Conference contribution

AN - SCOPUS:85180011684

T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

SP - 1903

EP - 1906

BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Y2 - 31 October 2023 through 3 November 2023

ER -

Lin SY, Chien MC, Meng ETK, Wang YC, Kuo YY, Lin CH. Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality. 於 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1903-1906. (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). doi: 10.1109/APSIPAASC58517.2023.10317187

Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality

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