A. Cunha
Impact in
- Health Informatics top 5%
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Retinal Imaging and Analysis
Papers in
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- Radiomics and Machine Learning in Medical Imaging 25
- Retinal Imaging and Analysis 14
- COVID-19 diagnosis using AI 14
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- Lung Cancer Diagnosis and Treatment 23
- Co-authors
- Joaquim J. Sousa (33 shared papers)Hélder P. Oliveira (21 shared papers)Tânia Pereira (16 shared papers)Raul Morais (13 shared papers)José Luís Costa (10 shared papers)Cláudia Freitas (11 shared papers)Venceslau Hespanhol (10 shared papers)Aurélio Campilho (9 shared papers)
In The Last Decade
A. Cunha
114 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 138
- Health Informatics 36
- Radiology, Nuclear Medicine and Imaging 426
- Pulmonary and Respiratory Medicine 271
- Ophthalmology 71
- Computer Vision and Pattern Recognition 163
Countries citing papers authored by A. Cunha
This map shows the geographic impact of A. Cunha'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 A. Cunha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Cunha more than expected).
Fields of papers citing papers by A. Cunha
This network shows the impact of papers produced by A. Cunha. 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 A. Cunha. The network helps show where A. Cunha may publish in the future.
Co-authors
The 25 scholars most cited alongside A. Cunha, 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 136 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 62 | |
| 2 | 2023 | 61 | |
| 3 | 2020 | 48 | |
| 4 | 2024 | 46 | |
| 5 | 2022 | 36 | |
| 6 | 2022 | 35 | |
| 7 | 2023 | 34 | |
| 8 | 2021 | 33 | |
| 9 | 2020 | 31 | |
| 10 | 2021 | 31 | |
| 11 | 2022 | 27 | |
| 12 | 2021 | 27 | |
| 13 | 2018 | 26 | |
| 14 | 2022 | 25 | |
| 15 | 2021 | 25 | |
| 16 | 2022 | 24 | |
| 17 | 2022 | 21 | |
| 18 | 2014 | 21 | |
| 19 | 2014 | 20 | |
| 20 | 2019 | 19 |
About A. Cunha
A. Cunha is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Computer Vision and Pattern Recognition and Plant Science, having authored 136 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (25 papers), Lung Cancer Diagnosis and Treatment (23 papers), AI in cancer detection (20 papers), Smart Agriculture and AI (17 papers), Colorectal Cancer Screening and Detection (16 papers), Retinal Imaging and Analysis (14 papers), COVID-19 diagnosis using AI (14 papers) and Horticultural and Viticultural Research (13 papers). The work is most often cited by research in Health Informatics (36 citations), Radiology, Nuclear Medicine and Imaging (426 citations), Pulmonary and Respiratory Medicine (271 citations), Ophthalmology (71 citations) and Computer Vision and Pattern Recognition (163 citations). A. Cunha has collaborated with scholars based in Portugal, Brazil and Spain. Frequent co-authors include Joaquim J. Sousa, Hélder P. Oliveira, Tânia Pereira, Raul Morais, José Luís Costa, Cláudia Freitas, Venceslau Hespanhol, Aurélio Campilho, Francisco Silva and Ana Maria Mendonça. Their work appears in journals such as Applied Sciences, IEEE Access, Expert Systems with Applications, Sensors and Electronics.
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.