Deevyankar Agarwal
Impact in
- Health Informatics top 5%
- Neurology top 10%
- Brain Tumor Detection and Classification
Papers in
-
- Machine Learning in Healthcare 3
- AI in cancer detection 2
- Artificial Intelligence in Games 1
- Co-authors
- Isabel de la Torre Díez (7 shared papers)Gonçalo Marques (3 shared papers)Manuel Franco (2 shared papers)Francisco Martín‐Rodriguez (1 shared paper)Begonya García-Zapirain (1 shared paper)M. Álvaro Berbís (2 shared papers)Antonio Luna (2 shared papers)Moolchand Sharma (3 shared papers)
In The Last Decade
Deevyankar Agarwal
9 papers receiving 422 citations
Deevyankar Agarwal's Hit Papers
Peers
Comparison fields: 5 of 89
- Health Informatics 29
- Neurology 77
- Radiology, Nuclear Medicine and Imaging 172
- Artificial Intelligence 165
- Health Information Management 22
Countries citing papers authored by Deevyankar Agarwal
This map shows the geographic impact of Deevyankar Agarwal'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 Deevyankar Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deevyankar Agarwal more than expected).
Fields of papers citing papers by Deevyankar Agarwal
This network shows the impact of papers produced by Deevyankar Agarwal. 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 Deevyankar Agarwal. The network helps show where Deevyankar Agarwal may publish in the future.
Co-authors
The 18 scholars most cited alongside Deevyankar Agarwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network Hit paper breakdown → | 2020 | 283 |
| 2 | 2021 | 53 | |
| 3 | 2023 | 29 | |
| 4 | 2023 | 26 | |
| 5 | 2022 | 21 | |
| 6 | 2022 | 11 | |
| 7 | 2021 | 4 | |
| 8 | 2024 | 3 | |
| 9 | 2022 | 2 | |
| 10 | 2024 | 0 | |
| 11 | 2024 | 0 |
About Deevyankar Agarwal
Deevyankar Agarwal is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Psychiatry and Mental health and Health Information Management, having authored 11 papers that have together received 432 indexed citations. Recurring topics across this work include Dementia and Cognitive Impairment Research (3 papers), Machine Learning in Healthcare (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Artificial Intelligence in Healthcare (2 papers), Brain Tumor Detection and Classification (2 papers), AI in cancer detection (2 papers), Alzheimer's disease research and treatments (1 paper) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Health Informatics (29 citations), Neurology (77 citations), Radiology, Nuclear Medicine and Imaging (172 citations), Artificial Intelligence (165 citations) and Health Information Management (22 citations). Deevyankar Agarwal has collaborated with scholars based in Spain, Oman and Portugal. Frequent co-authors include Isabel de la Torre Díez, Gonçalo Marques, Manuel Franco, Francisco Martín‐Rodriguez, Begonya García-Zapirain, M. Álvaro Berbís, Antonio Luna, Moolchand Sharma, Julién Brito Ballester and Vivían Lipari. Their work appears in journals such as Sensors, Applied Soft Computing, Journal of Medical Systems, Multimedia Tools and Applications 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.