U. Raghavendra
- Radiology, Nuclear Medicine and Imaging top 1%
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Cognitive Neuroscience top 5%
- Ophthalmology top 1%
- Co-authors
- U. Rajendra AcharyaAnjan GudigarJen Hong TanHamido FujitaYuki HagiwaraSulatha V. BhandaryShu Lih OhEdward J. Ciaccio
- Topics
- Brain Tumor Detection and Classification (15 papers)Retinal Imaging and Analysis (10 papers)AI in cancer detection (10 papers)
In The Last Decade
U. Raghavendra
80 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 168
- Radiology, Nuclear Medicine and Imaging 1.2k
- Computer Vision and Pattern Recognition 854
- Artificial Intelligence 586
- Cognitive Neuroscience 500
- Ophthalmology 459
Countries citing papers authored by U. Raghavendra
This map shows the geographic impact of U. Raghavendra'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 U. Raghavendra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites U. Raghavendra more than expected).
Fields of papers citing papers by U. Raghavendra
This network shows the impact of papers produced by U. Raghavendra. 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 U. Raghavendra. The network helps show where U. Raghavendra may publish in the future.
Co-authorship network of co-authors of U. Raghavendra
This figure shows the co-authorship network connecting the top 25 collaborators of U. Raghavendra. A scholar is included among the top collaborators of U. Raghavendra 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 U. Raghavendra. U. Raghavendra 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 | 3 | |
| 3 | 2 | |
| 4 | 7 | |
| 5 | 5 | |
| 6 | 6 | |
| 7 | 9 | |
| 8 | 4 | |
| 9 | 2 | |
| 10 | 9 | |
| 11 | 4 | |
| 12 | 34 | |
| 13 | 44 | |
| 14 | 37 | |
| 15 | 22 | |
| 16 | 11 | |
| 17 | Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus imagesbreakdown → | 336 |
| 18 | Illumination Invariant Data Cost using Modified Census Transform | 1 |
| 19 | 1 | |
| 20 | Saliva C- reactive protein levels in patients with acute urticaria | 5 |
About U. Raghavendra
U. Raghavendra is a scholar working on Neurology, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 84 papers that have together received 3.1k indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (15 papers), Retinal Imaging and Analysis (10 papers) and AI in cancer detection (10 papers). The work is most often cited by research in Health Informatics (92 citations), Radiology, Nuclear Medicine and Imaging (1.2k citations) and Neurology (422 citations). U. Raghavendra has collaborated with scholars based in India, Singapore and Malaysia. Frequent co-authors include U. Rajendra Acharya, Anjan Gudigar, Jen Hong Tan, Hamido Fujita, Yuki Hagiwara, Sulatha V. Bhandary, Shu Lih Oh, Edward J. Ciaccio, M. Murugappan and N. Arunkumar. Their work appears in journals such as IEEE Access, Sensors 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.