Lingyu Chen

854 citations
35 papers · 613 · h-index 13

Impact in

Papers in

Lingyu Chen

33 papers receiving 589 citations

Peers

Lingyu Chen
Comparison fields: 5 of 145
  • Neurology 84
  • Computer Vision and Pattern Recognition 174
  • Environmental Engineering 109
  • Health Informatics 8
  • Ocean Engineering 58
Replace Linyuan Wang with:
Linyuan Wang China
Chih‐Yuan Huang Taiwan
Miguel Guevara Portugal
Hongjun Jia United States
Mingliang Wang China
Xin Feng China
Shahab Abdulla Australia
Yu Zhu China
Jun‐Hyun Park South Korea
Lingyu Chen relative to Linyuan Wang China Linyuan Wang's profile →
Citations per field
00.5×5.3×
Linyuan Wang · 1×
Citations per year

Countries citing papers authored by Lingyu Chen

Since Specialization
Citations

This map shows the geographic impact of Lingyu Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lingyu Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lingyu Chen more than expected).

Fields of papers citing papers by Lingyu Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lingyu Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lingyu Chen. The network helps show where Lingyu Chen may publish in the future.

Co-authors

The 25 scholars most cited alongside Lingyu Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lingyu Chen Line = papers co-authored together Lingyu Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014113
2 201871
3 201550
4 202147
5 201144
6 202338
7 202230
8 202128
9 202026
10 201625
11 202322
12 202119
13 202316
14 201512
15 20238
16 20037
17 20207
18 20196
19 20106
20 20245

About Lingyu Chen

Lingyu Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Environmental Engineering, Surgery and Cognitive Neuroscience, having authored 35 papers that have together received 613 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Advanced Neural Network Applications (4 papers), AI in cancer detection (3 papers), Menstrual Health and Disorders (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Microbial Fuel Cells and Bioremediation (2 papers), Neural Networks and Applications (2 papers) and Pregnancy-related medical research (2 papers). The work is most often cited by research in Neurology (84 citations), Computer Vision and Pattern Recognition (174 citations), Environmental Engineering (109 citations), Health Informatics (8 citations) and Ocean Engineering (58 citations). Lingyu Chen has collaborated with scholars based in China, Taiwan and United States. Frequent co-authors include Jiafei Zhao, Jijun Tong, Lurong Jiang, Yingjie Zhao, Peng Zhang, Yi Zhang, Yuechao Zhao, Yu Liu, Dayong Wang and Lanlan Jiang. Their work appears in journals such as Computer Methods and Programs in Biomedicine, IEEE Transactions on Medical Imaging, Neurochemical Research, Frontiers in Aging Neuroscience and Electronics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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