Tunç Aşuroğlu

964 citations
47 papers · 475 indexed · h-index 11
Topics
Machine Learning in Healthcare (5 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)Hydrological Forecasting Using AI (3 papers)
Journals
SHILAP Revista de lepidopterologíaScientific ReportsIEEE Access
Partner nations
TürkiyeFinlandNorway

In The Last Decade

Tunç Aşuroğlu

42 papers receiving 458 citations

Peers

Tunç Aşuroğlu
Comparison fields: 5 of 133
  • Artificial Intelligence 108
  • Biomedical Engineering 101
  • Computer Vision and Pattern Recognition 77
  • Neurology 63
  • Radiology, Nuclear Medicine and Imaging 51
Replace Koray Açıcı with:
Koray Açıcı Türkiye
Phillip Chlap Australia
D. R. Sarvamangala India
Haseeb Hassan China
Anupama Bhan India
H.P. Ng Singapore
Seda Arslan Tuncer Türkiye
Yung‐Kyun Noh South Korea
Andrzej Skalski Poland
S. Suganyadevi India
Tunç Aşuroğlu relative to Koray Açıcı Türkiye Koray Açıcı's profile →
Citations per field
00.5×1.5×
Koray Açıcı · 1×
Citations per year

Countries citing papers authored by Tunç Aşuroğlu

Since Specialization
Citations

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

Fields of papers citing papers by Tunç Aşuroğlu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tunç Aşuroğlu

This figure shows the co-authorship network connecting the top 25 collaborators of Tunç Aşuroğlu. A scholar is included among the top collaborators of Tunç Aşuroğlu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tunç Aşuroğlu. Tunç Aşuroğlu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 6
4 2
5 1
6 2
7 3
8 1
9 7
10 2
11 54
12 8
13 12
14 7
15 18
16 7
17 25
18 4
19 30
20 33

About Tunç Aşuroğlu

Tunç Aşuroğlu is a scholar working on Health Informatics, Health Information Management and Computer Vision and Pattern Recognition, having authored 47 papers that have together received 475 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Hydrological Forecasting Using AI (3 papers). The work is most often cited by research in Health Informatics (11 citations), Oral Surgery (37 citations) and Health Information Management (23 citations). Tunç Aşuroğlu has collaborated with scholars based in Türkiye, Finland and Norway. Frequent co-authors include Koray Açıcı, Hasan Oğul, Mehmet Serdar Güzel, Erkan Bostancı, Çağatay Berke Erdaş, Hamit Erdem, M. Kılınç Toprak, Ricardo Colomo‐Palacios, Alejandro Baldominos and Engin Koçak. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

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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