Jun Sang

1.7k citations
73 papers · 1.2k · 1 hit paper · h-index 18

Impact in

Papers in

Jun Sang

59 papers receiving 1.1k citations

Jun Sang's Hit Papers

Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies 2019 · 236 citations
2360+2+4Years since publication50100150200

Peers

Jun Sang
Comparison fields: 5 of 107
  • Computer Vision and Pattern Recognition 452
  • Management Information Systems 127
  • Information Systems 269
  • Health Informatics 15
  • Radiology, Nuclear Medicine and Imaging 253
Replace Jie Yang with:
Jie Yang China
Md Whaiduzzaman Bangladesh
Dheyaa Ahmed Ibrahim Iraq
Qasem Abu Al‐Haija Jordan
Rahul Kumar Singh India
Maria Ganzha Poland
Denis A. Pustokhin Russia
Mukesh Soni India
Muhammad Muzammal Pakistan
M. M. A. Hashem Bangladesh
Jun Sang relative to Jie Yang China Jie Yang's profile →
Citations per field
00.5×4.2×
Jie Yang · 1×
Citations per year

Countries citing papers authored by Jun Sang

Since Specialization
Citations

This map shows the geographic impact of Jun Sang'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 Jun Sang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Sang more than expected).

Fields of papers citing papers by Jun Sang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Sang. 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 Jun Sang. The network helps show where Jun Sang may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Sang, 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 Jun Sang Line = papers co-authored together Jun Sang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 73 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies
Hit paper breakdown →
2019236
2 2018156
3 201977
4 202172
5 201757
6 201851
7 201940
8 202139
9 201936
10 201935
11 201132
12 201927
13 201827
14 201926
15 201824
16 202221
17 201920
18 201919
19 201716
20 202011

About Jun Sang

Jun Sang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications, having authored 73 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (17 papers), Video Surveillance and Tracking Methods (17 papers), Chaos-based Image/Signal Encryption (17 papers), Anomaly Detection Techniques and Applications (11 papers), Digital Media Forensic Detection (10 papers), Software Engineering Techniques and Practices (7 papers), Software Engineering Research (7 papers) and COVID-19 diagnosis using AI (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (452 citations), Management Information Systems (127 citations), Information Systems (269 citations), Health Informatics (15 citations) and Radiology, Nuclear Medicine and Imaging (253 citations). Jun Sang has collaborated with scholars based in China, United States and Pakistan. Frequent co-authors include Haibo Hu, Mohammad S. Alam, Hong Xiang, Bin Cai, Nasrullah Nasrullah, Muhammad Azeem Akbar, Muhammad Mateen, Arif Ali Khan, Zhongyuan Wu and Qian Zhang. Their work appears in journals such as IEEE Access, Sensors, Pattern Recognition Letters, Biomedical Signal Processing and Control and Optik.

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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