S. N. Kumar
Impact in
- Neurology top 10%
- Brain Tumor Detection and Classification
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- Medical Image Segmentation Techniques
- Advanced Data Compression Techniques
Papers in
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- Medical Image Segmentation Techniques 22
- Image and Signal Denoising Methods 12
- Advanced Data Compression Techniques 11
- Image Retrieval and Classification Techniques 6
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- AI in cancer detection 8
- Co-authors
- A. Lenin Fred (32 shared papers)Sujatha Krishnamoorthy (2 shared papers)Balázs Gulyás (13 shared papers)A. Ahilan (2 shared papers)Gokulnath Chandra Babu (1 shared paper)C. Agees Kumar (1 shared paper)P. Parthasarathy (1 shared paper)Priyan Malarvizhi Kumar (1 shared paper)
In The Last Decade
S. N. Kumar
57 papers receiving 530 citations
Peers
Comparison fields: 5 of 106
- Neurology 81
- Computer Vision and Pattern Recognition 208
- Media Technology 40
- Radiology, Nuclear Medicine and Imaging 80
- Health Information Management 17
Countries citing papers authored by S. N. Kumar
This map shows the geographic impact of S. N. Kumar'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 S. N. Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. N. Kumar more than expected).
Fields of papers citing papers by S. N. Kumar
This network shows the impact of papers produced by S. N. Kumar. 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 S. N. Kumar. The network helps show where S. N. Kumar may publish in the future.
Co-authors
The 25 scholars most cited alongside S. N. Kumar, 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 71 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 122 | |
| 2 | 2019 | 56 | |
| 3 | 2022 | 54 | |
| 4 | 2018 | 37 | |
| 5 | 2019 | 23 | |
| 6 | 2020 | 22 | |
| 7 | 2018 | 22 | |
| 8 | 2018 | 21 | |
| 9 | 2023 | 19 | |
| 10 | 2020 | 15 | |
| 11 | 2016 | 12 | |
| 12 | 2020 | 11 | |
| 13 | 2019 | 11 | |
| 14 | 2018 | 11 | |
| 15 | 2022 | 9 | |
| 16 | 2021 | 9 | |
| 17 | 2018 | 8 | |
| 18 | 2021 | 7 | |
| 19 | 2020 | 6 | |
| 20 | 2019 | 5 |
About S. N. Kumar
S. N. Kumar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Neurology and Biomedical Engineering, having authored 71 papers that have together received 555 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (22 papers), Brain Tumor Detection and Classification (13 papers), Image and Signal Denoising Methods (12 papers), Advanced Data Compression Techniques (11 papers), AI in cancer detection (8 papers), Image Retrieval and Classification Techniques (6 papers), Retinal Imaging and Analysis (5 papers) and COVID-19 diagnosis using AI (5 papers). The work is most often cited by research in Neurology (81 citations), Computer Vision and Pattern Recognition (208 citations), Media Technology (40 citations), Radiology, Nuclear Medicine and Imaging (80 citations) and Health Information Management (17 citations). S. N. Kumar has collaborated with scholars based in India, Singapore and Malaysia. Frequent co-authors include A. Lenin Fred, Sujatha Krishnamoorthy, Balázs Gulyás, A. Ahilan, Gokulnath Chandra Babu, C. Agees Kumar, P. Parthasarathy, Priyan Malarvizhi Kumar, Parasuraman Padmanabhan and Gunasekaran Manogaran. Their work appears in journals such as Multimedia Systems, Measurement, Food Biophysics, IEEE Access and Computers, materials & continua/Computers, materials & continua (Print).
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.