Fernando Collado‐Mesa

1.2k citations
41 papers · 824 · h-index 14

Impact in

Papers in

Fernando Collado‐Mesa

39 papers receiving 792 citations

Peers

Fernando Collado‐Mesa
Comparison fields: 5 of 119
  • Health Informatics 94
  • Dermatology 118
  • Radiology, Nuclear Medicine and Imaging 180
  • Neurology 46
  • Artificial Intelligence 166
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Harrison X. Bai United States
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Citations per field
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Citations per year

Countries citing papers authored by Fernando Collado‐Mesa

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Collado‐Mesa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando Collado‐Mesa. 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 Fernando Collado‐Mesa. The network helps show where Fernando Collado‐Mesa may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021121
2 201899
3 200490
4 200773
5 202253
6 199953
7 202052
8 200448
9 201430
10 201420
11 200417
12 202315
13
[Prevalence of Parkinson disease in an urban area of the Ciudad de La Habana province, Cuba. Door-to-door population study].
200014
14 202014
15 199713
16 200413
17 201212
18 201310
19 200410
20 20179

About Fernando Collado‐Mesa

Fernando Collado‐Mesa is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Oncology, Pathology and Forensic Medicine and Dermatology, having authored 41 papers that have together received 824 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Breast Cancer Treatment Studies (5 papers), Breast Lesions and Carcinomas (4 papers), Medical Imaging Techniques and Applications (3 papers), Hepatitis C virus research (3 papers), Cancer and Skin Lesions (3 papers) and Diabetes and associated disorders (2 papers). The work is most often cited by research in Health Informatics (94 citations), Dermatology (118 citations), Radiology, Nuclear Medicine and Imaging (180 citations), Neurology (46 citations) and Artificial Intelligence (166 citations). Fernando Collado‐Mesa has collaborated with scholars based in United States, Saudi Arabia and Cuba. Frequent co-authors include Mohamed Abdel-Mottaleb, Manal Alghamdi, Shasa Hu, Fangchao Ma, Robert S. Kirsner, Kris Arheart, Monica Yepes, Jose Net, Kristopher L. Arheart and Robert S. Kirsner. Their work appears in journals such as The Breast Journal, Memórias do Instituto Oswaldo Cruz, Breast Disease, PeerJ Computer Science and Cancer Epidemiology Biomarkers & Prevention.

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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