Deep Gupta

2.1k citations
61 papers · 1.3k · h-index 24

Impact in

Papers in

Deep Gupta

58 papers receiving 1.3k citations

Peers

Deep Gupta
Comparison fields: 5 of 115
  • Media Technology 309
  • Health Information Management 110
  • Computer Vision and Pattern Recognition 394
  • Health Informatics 22
  • Cardiology and Cardiovascular Medicine 276
Replace Mainak Biswas with:
Mainak Biswas India
Timothy J. W. Dawes United Kingdom
Spyretta Golemati Greece
Tomasz Markiewicz Poland
Muthu Rama Krishnan Mookiah Singapore
Xiangrong Zhou Japan
Kristen M. Meiburger Italy
Christos P. Loizou Cyprus
Keerthana Prasad India
Deep Gupta relative to Mainak Biswas India Mainak Biswas's profile →
Citations per field
00.5×2.9×
Mainak Biswas · 1×
Citations per year

Countries citing papers authored by Deep Gupta

Since Specialization
Citations

This map shows the geographic impact of Deep Gupta'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 Deep Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Gupta more than expected).

Fields of papers citing papers by Deep Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deep Gupta. 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 Deep Gupta. The network helps show where Deep Gupta may publish in the future.

Co-authors

The 25 scholars most cited alongside Deep Gupta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Deep Gupta Line = papers co-authored together Deep Gupta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015120
2 201666
3 201960
4 201959
5 201852
6 202047
7 202045
8 202043
9 202143
10 201440
11 202039
12 201932
13 202032
14 201931
15 201931
16 201830
17 202029
18 201929
19 201928
20 201427

About Deep Gupta

Deep Gupta is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging, having authored 61 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Image Fusion Techniques (23 papers), Image and Signal Denoising Methods (19 papers), Cardiovascular Health and Disease Prevention (13 papers), Advanced Image Processing Techniques (8 papers), Cerebrovascular and Carotid Artery Diseases (8 papers), Image Enhancement Techniques (6 papers), Cardiac Imaging and Diagnostics (5 papers) and Medical Image Segmentation Techniques (4 papers). The work is most often cited by research in Media Technology (309 citations), Health Information Management (110 citations), Computer Vision and Pattern Recognition (394 citations), Health Informatics (22 citations) and Cardiology and Cardiovascular Medicine (276 citations). Deep Gupta has collaborated with scholars based in India, Italy and United States. Frequent co-authors include R. S. Anand, Ankush D. Jamthikar, Barjeev Tyagi, Sneha Singh, John R. Laird, Jasjit S. Suri, Narendra N. Khanna, Luca Saba, Sophie Mavrogeni and George D. Kitas. Their work appears in journals such as Biomedical Signal Processing and Control, Computers in Biology and Medicine, Current Atherosclerosis Reports, IEEE Transactions on Instrumentation and Measurement and International Journal of Imaging Systems and Technology.

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.

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