Şaban Öztürk
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Oncology
- Electrical and Electronic Engineering
- Co-authors
- Bayram AkdemïrUmut ÖzkayaTolga ÇukurKemal PolatMajid NourMücahid BarstuğanHatem F. SindiMuhyaddin Rawa
- Topics
- AI in cancer detection (19 papers)Advanced Image and Video Retrieval Techniques (12 papers)Image Retrieval and Classification Techniques (10 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingHealth Informatics
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsInformation Sciences
- Partner nations
- TürkiyeSaudi ArabiaIndia
In The Last Decade
Şaban Öztürk
60 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 148
- Artificial Intelligence 570
- Computer Vision and Pattern Recognition 537
- Radiology, Nuclear Medicine and Imaging 456
- Oncology 185
- Electrical and Electronic Engineering 153
Countries citing papers authored by Şaban Öztürk
This map shows the geographic impact of Şaban Öztürk'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 Şaban Öztürk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Şaban Öztürk more than expected).
Fields of papers citing papers by Şaban Öztürk
This network shows the impact of papers produced by Şaban Öztürk. 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 Şaban Öztürk. The network helps show where Şaban Öztürk may publish in the future.
Co-authorship network of co-authors of Şaban Öztürk
This figure shows the co-authorship network connecting the top 25 collaborators of Şaban Öztürk. A scholar is included among the top collaborators of Şaban Öztürk 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 Şaban Öztürk. Şaban Öztürk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 11 | |
| 3 | 11 | |
| 4 | 3 | |
| 5 | 30 | |
| 6 | 7 | |
| 7 | Adaptive diffusion priors for accelerated MRI reconstructionbreakdown → | 140 |
| 8 | 11 | |
| 9 | 3 | |
| 10 | 52 | |
| 11 | Coronavirus (Covid-19) classification using CT images by machine learning methods | 1 |
| 12 | 33 | |
| 13 | 2 | |
| 14 | 3 | |
| 15 | 76 | |
| 16 | 1 | |
| 17 | 12 | |
| 18 | 107 | |
| 19 | 31 | |
| 20 | 1 |
About Şaban Öztürk
Şaban Öztürk is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence, having authored 63 papers that have together received 1.6k indexed citations. Recurring topics across this work include AI in cancer detection (19 papers), Advanced Image and Video Retrieval Techniques (12 papers) and Image Retrieval and Classification Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (537 citations), Radiology, Nuclear Medicine and Imaging (456 citations) and Health Informatics (26 citations). Şaban Öztürk has collaborated with scholars based in Türkiye, Saudi Arabia and India. Frequent co-authors include Bayram Akdemïr, Umut Özkaya, Tolga Çukur, Kemal Polat, Majid Nour, Mücahid Barstuğan, Hatem F. Sindi, Muhyaddin Rawa, Muzaffer Özbey and Salman Ul Hassan Dar. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Information Sciences.
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.