Devvi Sarwinda
Impact in
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- Artificial Intelligence in Healthcare
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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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- Imbalanced Data Classification Techniques 9
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- Machine Learning in Bioinformatics 15
- Gene expression and cancer classification 15
- Co-authors
- Alhadi Bustamam (44 shared papers)Pinkie Anggia (1 shared paper)Titin Siswantining (20 shared papers)Aniati Murni Arymurthy (3 shared papers)Zuherman Rustam (5 shared papers)Arry Yanuar (7 shared papers)Ari Wibisono (4 shared papers)Wibowo Mangunwardoyo (4 shared papers)
In The Last Decade
Devvi Sarwinda
74 papers receiving 895 citations
Devvi Sarwinda's Hit Papers
Peers
Comparison fields: 5 of 146
- Health Information Management 93
- Radiology, Nuclear Medicine and Imaging 301
- Neurology 107
- Computer Vision and Pattern Recognition 245
- Artificial Intelligence 366
Countries citing papers authored by Devvi Sarwinda
This map shows the geographic impact of Devvi Sarwinda'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 Devvi Sarwinda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Devvi Sarwinda more than expected).
Fields of papers citing papers by Devvi Sarwinda
This network shows the impact of papers produced by Devvi Sarwinda. 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 Devvi Sarwinda. The network helps show where Devvi Sarwinda may publish in the future.
Co-authors
The 20 scholars most cited alongside Devvi Sarwinda, 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 82 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer Hit paper breakdown → | 2021 | 357 |
| 2 | 2017 | 40 | |
| 3 | 2016 | 35 | |
| 4 | 2020 | 34 | |
| 5 | 2013 | 25 | |
| 6 | 2019 | 21 | |
| 7 | 2020 | 19 | |
| 8 | 2021 | 19 | |
| 9 | 2018 | 17 | |
| 10 | 2020 | 16 | |
| 11 | 2013 | 16 | |
| 12 | 2019 | 15 | |
| 13 | 2018 | 15 | |
| 14 | 2021 | 15 | |
| 15 | 2020 | 14 | |
| 16 | 2019 | 14 | |
| 17 | 2017 | 13 | |
| 18 | 2021 | 12 | |
| 19 | 2021 | 12 | |
| 20 | 2019 | 12 |
About Devvi Sarwinda
Devvi Sarwinda is a scholar working on Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging, Health Information Management and Computer Vision and Pattern Recognition, having authored 82 papers that have together received 953 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (15 papers), Gene expression and cancer classification (15 papers), Artificial Intelligence in Healthcare (14 papers), Retinal Imaging and Analysis (13 papers), Computational Drug Discovery Methods (10 papers), Imbalanced Data Classification Techniques (9 papers), Digital Imaging for Blood Diseases (7 papers) and Data Mining and Machine Learning Applications (7 papers). The work is most often cited by research in Health Information Management (93 citations), Radiology, Nuclear Medicine and Imaging (301 citations), Neurology (107 citations), Computer Vision and Pattern Recognition (245 citations) and Artificial Intelligence (366 citations). Devvi Sarwinda has collaborated with scholars based in Indonesia and Canada. Frequent co-authors include Alhadi Bustamam, Pinkie Anggia, Titin Siswantining, Aniati Murni Arymurthy, Zuherman Rustam, Arry Yanuar, Ari Wibisono, Wibowo Mangunwardoyo, Petrus Mursanto and Yekti Widyaningsih. Their work appears in journals such as Journal Of Big Data, Mathematical Biosciences & Engineering, Knowledge-Based Systems, Journal of Artificial Intelligence and Soft Computing Research and Symmetry.
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.