A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023)
Dublin Core
Title
A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023)
Subject
breast cancer; deep learning; image segmentation; bibliometric; WOS
Description
The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer
segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection
(WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a
substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer
images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a
consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords
from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak
in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well.
This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and
convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the
utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic
having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the
field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the
highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this
study
segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection
(WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a
substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer
images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a
consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords
from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak
in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well.
This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and
convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the
utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic
having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the
field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the
highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this
study
Creator
Agus Perdana Windarto, Anjar Wanto, Solikhun, Ronal Watrianthos
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
October 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Agus Perdana Windarto, Anjar Wanto, Solikhun, Ronal Watrianthos, “A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023),” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10086.