Peter Appiahene
Impact in
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- Artificial Intelligence in Healthcare
Papers in
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- AI in cancer detection 5
- Sentiment Analysis and Opinion Mining 3
- Anomaly Detection Techniques and Applications 3
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- Digital Imaging for Blood Diseases 7
- Co-authors
- Emmanuel Timmy Donkoh (6 shared papers)Yaw Marfo Missah (4 shared papers)V. Vijayakumar (7 shared papers)Giovanni Dimauro (2 shared papers)Tao Zhang (1 shared paper)Rosalia Maglietta (1 shared paper)Samuel Boateng (5 shared papers)Mukesh Prasad (2 shared papers)
In The Last Decade
Peter Appiahene
41 papers receiving 477 citations
Peers
Comparison fields: 5 of 108
- Health Information Management 51
- Health Informatics 8
- Management Science and Operations Research 66
- Computer Science Applications 28
- Artificial Intelligence 124
Countries citing papers authored by Peter Appiahene
This map shows the geographic impact of Peter Appiahene'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 Peter Appiahene with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Appiahene more than expected).
Fields of papers citing papers by Peter Appiahene
This network shows the impact of papers produced by Peter Appiahene. 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 Peter Appiahene. The network helps show where Peter Appiahene may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Appiahene, 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 51 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 55 | |
| 2 | 2022 | 51 | |
| 3 | 2023 | 41 | |
| 4 | 2019 | 35 | |
| 5 | 2023 | 32 | |
| 6 | 2023 | 31 | |
| 7 | 2023 | 28 | |
| 8 | 2020 | 28 | |
| 9 | 2023 | 23 | |
| 10 | 2023 | 17 | |
| 11 | 2023 | 15 | |
| 12 | 2023 | 14 | |
| 13 | 2022 | 14 | |
| 14 | 2024 | 13 | |
| 15 | 2023 | 12 | |
| 16 | 2023 | 10 | |
| 17 | 2024 | 10 | |
| 18 | 2016 | 9 | |
| 19 | 2018 | 8 | |
| 20 | 2023 | 7 |
About Peter Appiahene
Peter Appiahene is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Radiology, Nuclear Medicine and Imaging and Hematology, having authored 51 papers that have together received 516 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (7 papers), Iron Metabolism and Disorders (5 papers), AI in cancer detection (5 papers), Sentiment Analysis and Opinion Mining (3 papers), Spectroscopy and Chemometric Analyses (3 papers), Flood Risk Assessment and Management (3 papers), Hydrology and Watershed Management Studies (3 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Health Information Management (51 citations), Health Informatics (8 citations), Management Science and Operations Research (66 citations), Computer Science Applications (28 citations) and Artificial Intelligence (124 citations). Peter Appiahene has collaborated with scholars based in Ghana, China and Australia. Frequent co-authors include Emmanuel Timmy Donkoh, Yaw Marfo Missah, V. Vijayakumar, Giovanni Dimauro, Tao Zhang, Rosalia Maglietta, Tao Zhang, Samuel Boateng, Mukesh Prasad and Richard Kwasi Bannor. Their work appears in journals such as Data in Brief, Future Internet, Journal Of Big Data, BioData Mining and Engineering Applications of 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.