Tripti Goel

74 papers receiving 1.2k citations

Tripti Goel's Hit Papers

Deep learning for brain age estimation: A systematic review 2023 · 89 citations
890+1+2Years since publication255075

Peers

Tripti Goel
Comparison fields: 5 of 101
  • Neurology 302
  • Health Informatics 43
  • Health Information Management 111
  • Radiology, Nuclear Medicine and Imaging 476
  • Computer Vision and Pattern Recognition 354
Replace R. Murugan with:
R. Murugan India
Dazhe Zhao China
Shamik Tiwari India
Peng Cao China
Siqi Liu China
Shivajirao M. Jadhav India
Devvi Sarwinda Indonesia
Samir S. Yadav India
Zhe Liu China
Lim Choo Min Singapore
Tripti Goel relative to R. Murugan India R. Murugan's profile →
Citations per field
00.5×4.7×
R. Murugan · 1×
Citations per year

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 81 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 →
202389
3 202170
4 202259
5 202154
6 202152
7 202347
8 202145
9 202339
10 202233
11 202130
12 202128
13 202427
14 202326
15 202226
16 202326
17 202225
18 202424
19 202024
20 202323

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 81 papers that have together received 1.2k indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (24 papers), Retinal Imaging and Analysis (17 papers), Face and Expression Recognition (11 papers), COVID-19 diagnosis using AI (11 papers), Retinal Diseases and Treatments (10 papers), Functional Brain Connectivity Studies (9 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Digital Imaging for Blood Diseases (7 papers). The work is most often cited by research in Neurology (302 citations), Health Informatics (43 citations), Health Information Management (111 citations), Radiology, Nuclear Medicine and Imaging (476 citations) and Computer Vision and Pattern Recognition (354 citations). Tripti Goel has collaborated with scholars based in India, Australia and South Korea. Frequent co-authors include R. Murugan, M. Tanveer, Rahul Sharma, Seyedali Mirjalili, Chin‐Teng Lin, Virendra P. Vishwakarma, Ponnuthurai Nagaratnam Suganthan, Vijay Nehra, Javier Del Ser and Yudong Zhang. Their work appears in journals such as Cognitive Computation, Biomedical Signal Processing and Control, Applied Soft Computing, Journal of Ambient Intelligence and Humanized Computing and Multimedia Tools and Applications.

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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