Dev Kumar Das
Impact in
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- Digital Imaging for Blood Diseases
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
Papers in
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- Digital Imaging for Blood Diseases 19
- Medical Image Segmentation Techniques 4
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- AI in cancer detection 9
- Co-authors
- Chandan Chakraborty (27 shared papers)Madhumala Ghosh (9 shared papers)Asok Kumar Maiti (13 shared papers)Mallika Pal (3 shared papers)Ajoy Kumar Ray (5 shared papers)Pranab Kumar Dutta (3 shared papers)Bhaskar Mitra (3 shared papers)Rajdeep Mukherjee (1 shared paper)
In The Last Decade
Dev Kumar Das
32 papers receiving 1k citations
Peers
Comparison fields: 5 of 68
- Computer Vision and Pattern Recognition 596
- Health Informatics 35
- Rehabilitation 48
- Artificial Intelligence 215
- Health Information Management 39
Countries citing papers authored by Dev Kumar Das
This map shows the geographic impact of Dev Kumar Das'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 Dev Kumar Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dev Kumar Das more than expected).
Fields of papers citing papers by Dev Kumar Das
This network shows the impact of papers produced by Dev Kumar Das. 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 Dev Kumar Das. The network helps show where Dev Kumar Das may publish in the future.
Co-authors
The 25 scholars most cited alongside Dev Kumar Das, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 200 | |
| 2 | 2014 | 103 | |
| 3 | 2010 | 88 | |
| 4 | 2018 | 88 | |
| 5 | 2015 | 73 | |
| 6 | 2006 | 55 | |
| 7 | 2014 | 53 | |
| 8 | 2016 | 40 | |
| 9 | 2015 | 37 | |
| 10 | 2010 | 36 | |
| 11 | 2012 | 33 | |
| 12 | 2018 | 30 | |
| 13 | 2011 | 29 | |
| 14 | 2017 | 29 | |
| 15 | 2010 | 24 | |
| 16 | 2011 | 21 | |
| 17 | 2012 | 21 | |
| 18 | 2019 | 19 | |
| 19 | 2010 | 14 | |
| 20 | 2017 | 14 |
About Dev Kumar Das
Dev Kumar Das is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Rehabilitation, Health Information Management and Radiology, Nuclear Medicine and Imaging, having authored 35 papers that have together received 1.1k indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (19 papers), AI in cancer detection (9 papers), Medical Image Segmentation Techniques (4 papers), Retinal Imaging and Analysis (2 papers), Wound Healing and Treatments (2 papers), Artificial Intelligence in Healthcare (2 papers), Brain Tumor Detection and Classification (1 paper) and Telemedicine and Telehealth Implementation (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (596 citations), Health Informatics (35 citations), Rehabilitation (48 citations), Artificial Intelligence (215 citations) and Health Information Management (39 citations). Dev Kumar Das has collaborated with scholars based in India, Spain and China. Frequent co-authors include Chandan Chakraborty, Madhumala Ghosh, Asok Kumar Maiti, Mallika Pal, Ajoy Kumar Ray, Pranab Kumar Dutta, Bhaskar Mitra, Rajdeep Mukherjee, Naseeb Singh and Dhiraj Manohar Dhane. Their work appears in journals such as Journal of Microscopy, Tissue and Cell, Journal of Medical Systems, Applied Soft Computing and Multidimensional Systems and Signal Processing.
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.