Ramya Mohan

26 papers receiving 158 citations

Peers

Ramya Mohan
Comparison fields: 5 of 52
  • Neurology 31
  • Health Information Management 16
  • Radiology, Nuclear Medicine and Imaging 59
  • General Dentistry 4
  • Periodontics 10
Replace Tassilo Wald with:
Tassilo Wald Germany
Guangzhou An Japan
Sana Salahuddin Pakistan
Chunliang Wang Sweden
A. Mohanarathinam India
Xiangzuo Huo China
Suresh Guluwadi Ethiopia
Aolun Li China
Tathagat Banerjee India
Tânia Melo Portugal
Ramya Mohan relative to Tassilo Wald Germany Tassilo Wald's profile →
Citations per field
00.5×3.3×
Tassilo Wald · 1×
Citations per year

Countries citing papers authored by Ramya Mohan

Since Specialization
Citations

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

Fields of papers citing papers by Ramya Mohan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Ramya Mohan, 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 Ramya Mohan Line = papers co-authored together Ramya Mohan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202228
2 202321
3 202221
4 20239
5 20228
6 20248
7 20248
8 20237
9 20246
10 20235
11 20255
12 20235
13 20244
14 20244
15 20244
16 20223
17 20243
18 20232
19 20212
20 20232

About Ramya Mohan

Ramya Mohan is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Neurology and Oral Surgery, having authored 27 papers that have together received 162 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (5 papers), Digital Imaging for Blood Diseases (4 papers), Brain Tumor Detection and Classification (4 papers), Dental Radiography and Imaging (4 papers), Retinal Imaging and Analysis (3 papers) and Lung Cancer Diagnosis and Treatment (3 papers). The work is most often cited by research in Neurology (31 citations), Health Information Management (16 citations), Radiology, Nuclear Medicine and Imaging (59 citations), General Dentistry (4 citations) and Periodontics (10 citations). Ramya Mohan has collaborated with scholars based in India, United Arab Emirates and Lebanon. Frequent co-authors include V. Rajinikanth, Seifedine Kadry, Arnab Majumdar, Orawit Thinnukool, Mathiyazhagan Narayanan, Baji Shaik, Mohammed Rafi Shaik, Ramalingam Karthik Raja, Mujeeb Khan and S. Prabha. Their work appears in journals such as Life, Biomolecules, Mathematical Biosciences & Engineering, Lecture notes in computer science and International Journal of Pattern Recognition and Artificial Intelligence.

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.

Explore authors with similar magnitude of impact