Azhar Imran
Impact in
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- Artificial Intelligence in Healthcare
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- Retinal Imaging and Analysis
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 10
- Sentiment Analysis and Opinion Mining 8
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- Retinal Imaging and Analysis 14
- COVID-19 diagnosis using AI 8
- Co-authors
- Jianqiang Li (16 shared papers)Jahanzaib Latif (6 shared papers)Shanshan Tu (4 shared papers)Faheem Akhtar (14 shared papers)Abdulkareem Alzahrani (16 shared papers)Yan Pei (8 shared papers)Chuangbai Xiao (3 shared papers)Anas Bilal (8 shared papers)
- Journals
- IEEE Access (15 papers)PLoS ONE (3 papers)Applied Sciences (2 papers)Multimedia Tools and Applications (2 papers)Biomedicines (2 papers)
- Partner nations
- PakistanChinaSaudi Arabia
In The Last Decade
Azhar Imran
72 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 129
- Health Information Management 137
- Radiology, Nuclear Medicine and Imaging 572
- Ophthalmology 200
- Neurology 164
- Health Informatics 27
Countries citing papers authored by Azhar Imran
This map shows the geographic impact of Azhar Imran'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 Azhar Imran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Azhar Imran more than expected).
Fields of papers citing papers by Azhar Imran
This network shows the impact of papers produced by Azhar Imran. 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 Azhar Imran. The network helps show where Azhar Imran may publish in the future.
Co-authors
The 25 scholars most cited alongside Azhar Imran, 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 78 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 111 | |
| 2 | 2020 | 108 | |
| 3 | 2022 | 89 | |
| 4 | 2019 | 79 | |
| 5 | 2019 | 72 | |
| 6 | 2022 | 72 | |
| 7 | 2022 | 70 | |
| 8 | 2022 | 57 | |
| 9 | 2024 | 56 | |
| 10 | 2022 | 56 | |
| 11 | 2022 | 50 | |
| 12 | 2020 | 44 | |
| 13 | 2020 | 41 | |
| 14 | 2022 | 39 | |
| 15 | 2020 | 39 | |
| 16 | 2023 | 39 | |
| 17 | 2024 | 35 | |
| 18 | 2018 | 31 | |
| 19 | 2020 | 27 | |
| 20 | 2019 | 26 |
About Azhar Imran
Azhar Imran is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications, Computer Vision and Pattern Recognition and Information Systems, having authored 78 papers that have together received 1.4k indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (14 papers), Digital Imaging for Blood Diseases (11 papers), AI in cancer detection (10 papers), Advanced Malware Detection Techniques (8 papers), COVID-19 diagnosis using AI (8 papers), Network Security and Intrusion Detection (8 papers), Sentiment Analysis and Opinion Mining (8 papers) and Artificial Intelligence in Healthcare (6 papers). The work is most often cited by research in Health Information Management (137 citations), Radiology, Nuclear Medicine and Imaging (572 citations), Ophthalmology (200 citations), Neurology (164 citations) and Health Informatics (27 citations). Azhar Imran has collaborated with scholars based in Pakistan, China and Saudi Arabia. Frequent co-authors include Jianqiang Li, Jahanzaib Latif, Shanshan Tu, Faheem Akhtar, Abdulkareem Alzahrani, Yan Pei, Chuangbai Xiao, Anas Bilal, Abdullah Almuhaimeed and Guangmin Sun. Their work appears in journals such as IEEE Access, PLoS ONE, Applied Sciences, Multimedia Tools and Applications and Biomedicines.
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.