Arpan Basu
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 3
- Natural Language Processing Techniques 2
- Hate Speech and Cyberbullying Detection 2
- Topic Modeling 2
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- COVID-19 diagnosis using AI 5
- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Ram Sarkar (8 shared papers)Erik Cuevas (2 shared papers)Avishek Garain (5 shared papers)Fabio Giampaolo (1 shared paper)M. Shamim Kaiser (1 shared paper)Mufti Mahmud (1 shared paper)Zong Woo Geem (1 shared paper)Gi-Tae Han (1 shared paper)
- Journals
- Neural Computing and Applications (2 papers)Applied Soft Computing (1 paper)Multimedia Tools and Applications (1 paper)Expert Systems with Applications (1 paper)ChemistrySelect (1 paper)
- Partner nations
- IndiaMexicoSouth Korea
In The Last Decade
Arpan Basu
13 papers receiving 274 citations
Peers
Comparison fields: 5 of 76
- Health Informatics 24
- Radiology, Nuclear Medicine and Imaging 146
- Artificial Intelligence 175
- Computer Vision and Pattern Recognition 60
- Health Information Management 9
Countries citing papers authored by Arpan Basu
This map shows the geographic impact of Arpan Basu'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 Arpan Basu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arpan Basu more than expected).
Fields of papers citing papers by Arpan Basu
This network shows the impact of papers produced by Arpan Basu. 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 Arpan Basu. The network helps show where Arpan Basu may publish in the future.
Co-authors
The 15 scholars most cited alongside Arpan Basu, 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 | 2021 | 60 | |
| 2 | 2022 | 52 | |
| 3 | 2021 | 48 | |
| 4 | 2022 | 40 | |
| 5 | 2021 | 37 | |
| 6 | 2020 | 13 | |
| 7 | 2019 | 8 | |
| 8 | 2019 | 8 | |
| 9 | 2022 | 5 | |
| 10 | 2024 | 4 | |
| 11 | 2019 | 4 | |
| 12 | 2019 | 3 | |
| 13 | 2023 | 1 |
About Arpan Basu
Arpan Basu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Media Technology and Organic Chemistry, having authored 13 papers that have together received 283 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (5 papers), AI in cancer detection (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Natural Language Processing Techniques (2 papers), Handwritten Text Recognition Techniques (2 papers), Hate Speech and Cyberbullying Detection (2 papers), Vehicle License Plate Recognition (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Health Informatics (24 citations), Radiology, Nuclear Medicine and Imaging (146 citations), Artificial Intelligence (175 citations), Computer Vision and Pattern Recognition (60 citations) and Health Information Management (9 citations). Arpan Basu has collaborated with scholars based in India, Mexico and South Korea. Frequent co-authors include Ram Sarkar, Erik Cuevas, Avishek Garain, Fabio Giampaolo, M. Shamim Kaiser, Mufti Mahmud, Zong Woo Geem, Gi-Tae Han, Showmik Bhowmik and Sudip Kumar Naskar. Their work appears in journals such as Neural Computing and Applications, Applied Soft Computing, Multimedia Tools and Applications, Expert Systems with Applications and ChemistrySelect.
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.