Geraldo Bráz
Impact in
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- Retinal Imaging and Analysis
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
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- Digital Imaging for Blood Diseases
- Image Retrieval and Classification Techniques
Papers in
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- Digital Imaging for Blood Diseases 20
- Advanced Neural Network Applications 8
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- AI in cancer detection 38
- Co-authors
- Anselmo Cardoso de Paiva (97 shared papers)Aristófanes Corrêa Silva (65 shared papers)João Dallyson Sousa de Almeida (78 shared papers)Marcelo Gattass (15 shared papers)Leonardo Martins (5 shared papers)João Otávio Bandeira Diniz (16 shared papers)Alexandre Cêsar Muniz de Oliveira (2 shared papers)Luana Batista da Cruz (8 shared papers)
In The Last Decade
Geraldo Bráz
112 papers receiving 945 citations
Peers
Comparison fields: 5 of 115
- Radiology, Nuclear Medicine and Imaging 374
- Computer Vision and Pattern Recognition 381
- Ophthalmology 126
- Health Informatics 17
- Artificial Intelligence 391
Countries citing papers authored by Geraldo Bráz
This map shows the geographic impact of Geraldo Bráz'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 Geraldo Bráz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geraldo Bráz more than expected).
Fields of papers citing papers by Geraldo Bráz
This network shows the impact of papers produced by Geraldo Bráz. 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 Geraldo Bráz. The network helps show where Geraldo Bráz may publish in the future.
Co-authors
The 25 scholars most cited alongside Geraldo Bráz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 141 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 77 | |
| 2 | 2009 | 68 | |
| 3 | 2021 | 56 | |
| 4 | 2017 | 40 | |
| 5 | 2021 | 38 | |
| 6 | 2013 | 32 | |
| 7 | 2022 | 30 | |
| 8 | 2021 | 29 | |
| 9 | 2018 | 25 | |
| 10 | 2023 | 24 | |
| 11 | 2021 | 23 | |
| 12 | 2018 | 21 | |
| 13 | 2019 | 21 | |
| 14 | 2016 | 21 | |
| 15 | 2021 | 18 | |
| 16 | 2018 | 18 | |
| 17 | 2023 | 18 | |
| 18 | 2018 | 18 | |
| 19 | 2018 | 17 | |
| 20 | 2022 | 17 |
About Geraldo Bráz
Geraldo Bráz is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Ophthalmology and Oncology, having authored 141 papers that have together received 990 indexed citations. Recurring topics across this work include AI in cancer detection (38 papers), Retinal Imaging and Analysis (25 papers), Digital Imaging for Blood Diseases (20 papers), Glaucoma and retinal disorders (20 papers), Radiomics and Machine Learning in Medical Imaging (16 papers), COVID-19 diagnosis using AI (13 papers), Advanced Neural Network Applications (8 papers) and Genital Health and Disease (7 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (374 citations), Computer Vision and Pattern Recognition (381 citations), Ophthalmology (126 citations), Health Informatics (17 citations) and Artificial Intelligence (391 citations). Geraldo Bráz has collaborated with scholars based in Brazil, Mexico and Portugal. Frequent co-authors include Anselmo Cardoso de Paiva, Aristófanes Corrêa Silva, João Dallyson Sousa de Almeida, Marcelo Gattass, Leonardo Martins, João Otávio Bandeira Diniz, Alexandre Cêsar Muniz de Oliveira, Luana Batista da Cruz, Rodrigo Veras and João Manuel R. S. Tavares. Their work appears in journals such as Multimedia Tools and Applications, Expert Systems with Applications, Computers in Biology and Medicine, Applied Sciences and IEEE Access.
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.