Ali Emre Kavur

1.3k citations
8 papers · 109 indexed · h-index 4
Topics
Medical Image Segmentation Techniques (3 papers)COVID-19 diagnosis using AI (3 papers)AI in cancer detection (2 papers)
Journals
SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsMedical Physics

In The Last Decade

Ali Emre Kavur

7 papers receiving 107 citations

Peers

Ali Emre Kavur
Comparison fields: 5 of 57
  • Radiology, Nuclear Medicine and Imaging 48
  • Artificial Intelligence 41
  • Computer Vision and Pattern Recognition 35
  • Biomedical Engineering 17
  • Surgery 13
Replace Philipp Schad with:
Philipp Schad Germany
Adi Szeskin Israel
Paul F. Jäger Germany
Matthias Perkonigg Austria
Rijo Roy Germany
Patrick Ferdinand Christ Germany
Bora Baydar Türkiye
Vivek Vaidya India
Hon Ho Yu China
Sarah Leclerc France
Ali Emre Kavur relative to Philipp Schad Germany Philipp Schad's profile →
Citations per field
00.5×
Philipp Schad · 1×
Citations per year

Countries citing papers authored by Ali Emre Kavur

Since Specialization
Citations

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

Fields of papers citing papers by Ali Emre Kavur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ali Emre Kavur. 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 Ali Emre Kavur. The network helps show where Ali Emre Kavur may publish in the future.

Co-authorship network of co-authors of Ali Emre Kavur

This figure shows the co-authorship network connecting the top 25 collaborators of Ali Emre Kavur. A scholar is included among the top collaborators of Ali Emre Kavur 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 Ali Emre Kavur. Ali Emre Kavur is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
#WorkIndexed citations
1 4
2 37
3 3
4 3
5 54
6 0
7 3
8 5

About Ali Emre Kavur

Ali Emre Kavur is a scholar working on Health Informatics, Computer Graphics and Computer-Aided Design and Radiology, Nuclear Medicine and Imaging, having authored 8 papers that have together received 109 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (3 papers), COVID-19 diagnosis using AI (3 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Health Informatics (9 citations), Radiology, Nuclear Medicine and Imaging (48 citations) and Computer Vision and Pattern Recognition (35 citations). Ali Emre Kavur has collaborated with scholars based in Türkiye, Switzerland and Mexico. Frequent co-authors include M. Alper Selver, Bora Baydar, Naciye Sinem Gezer, Mustafa Barış, Savaş Özkan, Oğuz Dıcle, Gözde Bozdağı Akar, Vivienn Weru, Tim Rädsch and Tobias Roß. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Medical Physics.

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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