Tripti Goel

74 papers receiving 1.1k citations

Hit Papers

Deep learning for brain age estimation: A systematic review 2023 · 84 citations
840+1+2Years since publication255075

Peers

Tripti Goel
Comparison fields: 5 of 105
  • Health Informatics 50
  • Neurology 302
  • Health Information Management 109
  • Radiology, Nuclear Medicine and Imaging 469
  • Computer Vision and Pattern Recognition 348
Replace R. Murugan with:
R. Murugan India
Siqi Liu China
Shamik Tiwari India
Sandeep Kumar Mathivanan India
Palani Thanaraj Krishnan India
Lim Choo Min Singapore
Muhammad Yaqub China
M. Suchetha India
Chua Kuang Chua Singapore
D. Selvathi India
Tripti Goel relative to R. Murugan India R. Murugan's profile →
Citations per field
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R. Murugan · 1×
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Countries citing papers authored by Tripti Goel

Since Specialization
Citations

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

Fields of papers citing papers by Tripti Goel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Tripti Goel, 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 Tripti Goel Line = papers co-authored together Tripti Goel links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020123
2
Deep learning for brain age estimation: A systematic review
Hit paper breakdown →
202384
3 202166
4 202153
5 202152
6 202252
7 202145
8 202342
9 202335
10 202129
11 202229
12 202128
13 202425
14 202325
15 202224
16 202024
17 202223
18 202322
19 202321
20 202220

About Tripti Goel

Tripti Goel is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Neurology, Artificial Intelligence and Ophthalmology, having authored 79 papers that have together received 1.1k indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (25 papers), Retinal Imaging and Analysis (17 papers), COVID-19 diagnosis using AI (11 papers), Face and Expression Recognition (11 papers), Retinal Diseases and Treatments (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), Machine Learning and ELM (8 papers) and Functional Brain Connectivity Studies (8 papers). The work is most often cited by research in Health Informatics (50 citations), Neurology (302 citations), Health Information Management (109 citations), Radiology, Nuclear Medicine and Imaging (469 citations) and Computer Vision and Pattern Recognition (348 citations). Tripti Goel has collaborated with scholars based in India, Australia and South Korea. Frequent co-authors include R. Murugan, M. Tanveer, Seyedali Mirjalili, Rahul Sharma, Virendra P. Vishwakarma, Chin‐Teng Lin, Vijay Nehra, Ponnuthurai Nagaratnam Suganthan, Javier Del Ser and Yudong Zhang. Their work appears in journals such as Applied Soft Computing, Cognitive Computation, Biomedical Signal Processing and Control, Information Fusion and Journal of Ambient Intelligence and Humanized Computing.

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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