Bibliometric Analysis of the Application of Convolutional Neural Network in Computer Vision

Chen, Huie; Deng, Zhenjie

doi:10.1109/access.2020.3019336

Cited by 12 publications

(5 citation statements)

References 44 publications

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“…Out of 10 research institutes, four of them are in China while two in Canada and one each are in the United States, India, the Netherlands, and Sweden. This finding implies that convolutional neural network and deep learning-related research has improved in China over these years [ 91 ].…”

Section: Resultsmentioning

confidence: 99%

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Khairi

Bakar

et al. 2021

Healthcare

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Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors’ keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.

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Section: Resultsmentioning

confidence: 99%

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Khairi

Bakar

et al. 2021

Healthcare

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“…There are also several bibliometric studies concerning other topics that mention object detection in their findings. Chen and Deng [4] conducted a bibliometric analysis regarding the application of Convolutional Neural Networks (CNNs) in CV. Published in 2020, their study concluded that object detection and location are the hottest areas in relation to CNN research.…”

Section: State-of-the-artmentioning

confidence: 99%

Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study

Yahia,

Lopes,

Lopes

2023

Electronics

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This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.

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“…Are widely applied in computer vision (Chen and Qi 2019). A typical CNN consists of convolutional layers, pooling layers, and fully connected layers.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

A systematic review of deep learning methods for modeling electrocardiograms during sleep

et al. 2022

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Sleep is one of the most important human physiological activities and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks using ECG signals. Therefore, in this review, we systematically identify recent research studies and analyze them from the perspectives of data, model, and task. We discuss the shortcomings, summarize the findings, and highlight the potential opportunities. For sleep-related tasks, many ECG-based DL methods produce more accurate results than traditional approaches by combining multiple signal features and model structures. Methods that are more interpretable, scalable, and transferable will become ubiquitous in the daily practice of medicine and ambient-assisted-living applications. This paper is the first systematic review of ECG-based DL methods for sleep tasks.

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Bibliometric Analysis of the Application of Convolutional Neural Network in Computer Vision

Cited by 12 publications

References 44 publications

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study

A systematic review of deep learning methods for modeling electrocardiograms during sleep

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