Ghada Atteia
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Aerospace Engineering top 10%
- Neurology top 10%
- Co-authors
- Nagwan Abdel SameeMichael CollinsMichael DenbinaAmel Ali AlhussanEl‐Sayed M. El‐kenawyAbdelhameed IbrahimAtif RizwanMona Jamjoom
- Topics
- AI in cancer detection (7 papers)Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers)COVID-19 diagnosis using AI (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsIEEE Transactions on Geoscience and Remote Sensing
- Partner nations
- Saudi ArabiaSouth KoreaEgypt
In The Last Decade
Ghada Atteia
37 papers receiving 634 citations
Peers
Comparison fields: 5 of 104
- Artificial Intelligence 212
- Computer Vision and Pattern Recognition 162
- Radiology, Nuclear Medicine and Imaging 157
- Aerospace Engineering 127
- Neurology 94
Countries citing papers authored by Ghada Atteia
This map shows the geographic impact of Ghada Atteia'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 Ghada Atteia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ghada Atteia more than expected).
Fields of papers citing papers by Ghada Atteia
This network shows the impact of papers produced by Ghada Atteia. 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 Ghada Atteia. The network helps show where Ghada Atteia may publish in the future.
Co-authorship network of co-authors of Ghada Atteia
This figure shows the co-authorship network connecting the top 25 collaborators of Ghada Atteia. A scholar is included among the top collaborators of Ghada Atteia 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 Ghada Atteia. Ghada Atteia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 5 | |
| 7 | 17 | |
| 8 | 5 | |
| 9 | 8 | |
| 10 | 3 | |
| 11 | 8 | |
| 12 | 14 | |
| 13 | 35 | |
| 14 | 41 | |
| 15 | 5 | |
| 16 | 40 | |
| 17 | 11 | |
| 18 | 32 | |
| 19 | 35 | |
| 20 | 20 |
About Ghada Atteia
Ghada Atteia is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology, having authored 45 papers that have together received 657 indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers) and COVID-19 diagnosis using AI (6 papers). The work is most often cited by research in Neurology (94 citations), Computer Vision and Pattern Recognition (162 citations) and Radiology, Nuclear Medicine and Imaging (157 citations). Ghada Atteia has collaborated with scholars based in Saudi Arabia, South Korea and Egypt. Frequent co-authors include Nagwan Abdel Samee, Michael Collins, Michael Denbina, Amel Ali Alhussan, El‐Sayed M. El‐kenawy, Abdelhameed Ibrahim, Atif Rizwan, Mona Jamjoom, Yasser M. Kadah and Mugahed A. Al–antari. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.
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.