Deep Gupta

2.0k total citations
59 papers, 1.2k citations indexed

About

Deep Gupta is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Deep Gupta has authored 59 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 23 papers in Media Technology and 20 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Deep Gupta's work include Advanced Image Fusion Techniques (23 papers), Image and Signal Denoising Methods (19 papers) and Cardiovascular Health and Disease Prevention (19 papers). Deep Gupta is often cited by papers focused on Advanced Image Fusion Techniques (23 papers), Image and Signal Denoising Methods (19 papers) and Cardiovascular Health and Disease Prevention (19 papers). Deep Gupta collaborates with scholars based in India, Italy and United States. Deep Gupta's co-authors include R. S. Anand, Ankush D. Jamthikar, Barjeev Tyagi, Sneha Singh, John R. Laird, Jasjit S. Suri, Narendra N. Khanna, Luca Saba, Vinod Kumar and Sophie Mavrogeni and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Instrumentation and Measurement and Neural Computing and Applications.

In The Last Decade

Deep Gupta

57 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Deep Gupta India	23	397	378	311	308	301	59	1.2k
Mainak Biswas India	14	416 1.0×	187 0.5×	470 1.5×	125 0.4×	233 0.8×	41	1.4k
Christos P. Loizou Cyprus	23	866 2.2×	755 2.0×	501 1.6×	299 1.0×	755 2.5×	101	2.0k
Timothy J. W. Dawes United Kingdom	17	534 1.3×	531 1.4×	615 2.0×	142 0.5×	168 0.6×	39	1.5k
M. Pantziaris Cyprus	21	784 2.0×	692 1.8×	480 1.5×	264 0.9×	704 2.3×	57	1.7k
Spyretta Golemati Greece	24	361 0.9×	819 2.2×	708 2.3×	52 0.2×	956 3.2×	77	2.0k
Tomasz Markiewicz Poland	16	299 0.8×	394 1.0×	230 0.7×	55 0.2×	164 0.5×	70	1.2k
Kristen M. Meiburger Italy	24	274 0.7×	400 1.1×	582 1.9×	36 0.1×	443 1.5×	90	1.5k
Xiangrong Zhou Japan	21	476 1.2×	146 0.4×	842 2.7×	86 0.3×	289 1.0×	94	1.8k
Luca Saba Italy	28	281 0.7×	533 1.4×	762 2.5×	26 0.1×	538 1.8×	67	1.9k
Muthu Rama Krishnan Mookiah Singapore	23	597 1.5×	210 0.6×	1.4k 4.4×	37 0.1×	141 0.5×	50	1.9k

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-authorship network of co-authors of Deep Gupta

This figure shows the co-authorship network connecting the top 25 collaborators of Deep Gupta. A scholar is included among the top collaborators of Deep Gupta based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Deep Gupta. Deep Gupta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Elbagory, Mohssen, Sahar El-Nahrawy, Alaa El-Dein Omara, et al.. (2025). Inclusion of key soil parameters in the modified contamination factor (MCF) model as a tool for assessing heavy metal pollution in agricultural soils. Scientific Reports. 15(1). 42974–42974.

Gupta, Deep, et al.. (2022). Optimized Multimodal Neurological Image Fusion Based on Low-Rank Texture Prior Decomposition and Super-Pixel Segmentation. IEEE Transactions on Instrumentation and Measurement. 71. 1–9. 6 indexed citations

Gupta, Deep, et al.. (2022). Multimodal image sensor fusion in a cascaded framework using optimized dual channel pulse coupled neural network. Journal of Ambient Intelligence and Humanized Computing. 14(9). 11985–12004. 1 indexed citations

Gupta, Deep, et al.. (2021). Multi‐scale decomposition‐based CT‐MR neurological image fusion using optimized bio‐inspired spiking neural model with meta‐heuristic optimization. International Journal of Imaging Systems and Technology. 31(4). 2170–2188. 3 indexed citations

Jamthikar, Ankush D., Deep Gupta, Laura E. Mantella, et al.. (2021). Ensemble Machine Learning and Its Validation for Prediction of Coronary Artery Disease and Acute Coronary Syndrome Using Focused Carotid Ultrasound. IEEE Transactions on Instrumentation and Measurement. 71. 1–10. 20 indexed citations

Puvvula, Anudeep, Ankush D. Jamthikar, Deep Gupta, et al.. (2020). Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease. Angiology. 71(6). 520–535. 20 indexed citations

Viswanathan, Vijay, Ankush D. Jamthikar, Deep Gupta, et al.. (2020). Does the Carotid Bulb Offer a Better 10-Year CVD/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study. Angiology. 71(10). 920–933. 13 indexed citations

Jamthikar, Ankush D., Deep Gupta, Anudeep Puvvula, et al.. (2020). Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging. Rheumatology International. 40(12). 1921–1939. 29 indexed citations

Singh, Sneha & Deep Gupta. (2020). Detail Enhanced Feature-Level Medical Image Fusion in Decorrelating Decomposition Domain. IEEE Transactions on Instrumentation and Measurement. 70. 1–9. 13 indexed citations

10.

Gupta, Deep, et al.. (2020). NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network. IET Image Processing. 14(16). 4291–4305. 9 indexed citations

11.

Singh, Sneha & Deep Gupta. (2020). Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network. International Journal of Imaging Systems and Technology. 31(2). 981–1001. 5 indexed citations

12.

Jamthikar, Ankush D., Deep Gupta, Elisa Cuadrado‐Godia, et al.. (2020). Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on “Integrated Vascular Age” instead of “Chronological Age”: a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts. Cardiovascular Diagnosis and Therapy. 10(4). 939–954. 15 indexed citations

13.

Jamthikar, Ankush D., Deep Gupta, Luca Saba, et al.. (2020). Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovascular Diagnosis and Therapy. 10(4). 919–938. 39 indexed citations

14.

Jamthikar, Ankush D., Deep Gupta, Amer M. Johri, et al.. (2020). Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. Journal of Medical Systems. 44(12). 208–208. 16 indexed citations

15.

Jamthikar, Ankush D., Deep Gupta, Luca Saba, et al.. (2020). Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound. Computers in Biology and Medicine. 126. 104043–104043. 35 indexed citations

16.

Khanna, Narendra N., Ankush D. Jamthikar, Tadashi Araki, et al.. (2019). Nonlinear model for the carotid artery disease 10‐year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study. Echocardiography. 36(2). 345–361. 30 indexed citations

17.

Jamthikar, Ankush D., Deep Gupta, Narendra N. Khanna, et al.. (2019). A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovascular Diagnosis and Therapy. 9(5). 420–430. 57 indexed citations

18.

Khanna, Narendra N., Ankush D. Jamthikar, Deep Gupta, et al.. (2019). Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0. Medical & Biological Engineering & Computing. 57(7). 1553–1566. 28 indexed citations

19.

Khanna, Narendra N., Ankush D. Jamthikar, Deep Gupta, et al.. (2019). Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study. Computers in Biology and Medicine. 105. 125–143. 30 indexed citations

20.

Kotsis, Vasilios, Ankush D. Jamthikar, Tadashi Araki, et al.. (2018). Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. Diabetes Research and Clinical Practice. 143. 322–331. 22 indexed citations

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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