K. Thomas Abraham
Impact in
- Artificial Intelligence top 5%
- AI in cancer detection
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- AI in cancer detection 2
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- Image Retrieval and Classification Techniques 2
- Co-authors
- U. Rajendra Acharya (5 shared papers)Karthikeyan Ganesan (3 shared papers)Lim Choo Min (2 shared papers)Chua Kuang Chua (2 shared papers)Kwan-Hoong Ng (1 shared paper)Choo Min Lim (3 shared papers)Jen Hong Tan (2 shared papers)Kuang Chua Chua (1 shared paper)
- Journals
- Digital Signal Processing (1 paper)IEEE Transactions on Instrumentation and Measurement (1 paper)Journal of Medical Systems (1 paper)Computer Methods and Programs in Biomedicine (1 paper)IEEE Reviews in Biomedical Engineering (1 paper)
- Partner nations
- SingaporeMalaysiaUnited States
In The Last Decade
K. Thomas Abraham
9 papers receiving 289 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 220
- Radiology, Nuclear Medicine and Imaging 138
- Computer Vision and Pattern Recognition 111
- Neurology 30
- Media Technology 16
Countries citing papers authored by K. Thomas Abraham
This map shows the geographic impact of K. Thomas Abraham'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 K. Thomas Abraham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. Thomas Abraham more than expected).
Fields of papers citing papers by K. Thomas Abraham
This network shows the impact of papers produced by K. Thomas Abraham. 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 K. Thomas Abraham. The network helps show where K. Thomas Abraham may publish in the future.
Co-authors
The 16 scholars most cited alongside K. Thomas Abraham, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 170 | |
| 2 | 2012 | 44 | |
| 3 | 2011 | 35 | |
| 4 | 2013 | 27 | |
| 5 | 2024 | 15 | |
| 6 | 2025 | 7 | |
| 7 | 2013 | 6 | |
| 8 | 2010 | 3 | |
| 9 | 2011 | 1 | |
| 10 | 2025 | 0 |
About K. Thomas Abraham
K. Thomas Abraham is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Mechanical Engineering and Biomedical Engineering, having authored 10 papers that have together received 308 indexed citations. Recurring topics across this work include Advanced machining processes and optimization (2 papers), Image Retrieval and Classification Techniques (2 papers), AI in cancer detection (2 papers), Music and Audio Processing (1 paper), COVID-19 diagnosis using AI (1 paper), Hand Gesture Recognition Systems (1 paper), Computational Drug Discovery Methods (1 paper) and Image Processing Techniques and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (220 citations), Radiology, Nuclear Medicine and Imaging (138 citations), Computer Vision and Pattern Recognition (111 citations), Neurology (30 citations) and Media Technology (16 citations). K. Thomas Abraham has collaborated with scholars based in Singapore, Malaysia and United States. Frequent co-authors include U. Rajendra Acharya, Karthikeyan Ganesan, Lim Choo Min, Chua Kuang Chua, Kwan-Hoong Ng, Choo Min Lim, Jen Hong Tan, Kuang Chua Chua, Zhengwei Yang and Sudesh Srivastav. Their work appears in journals such as Digital Signal Processing, IEEE Transactions on Instrumentation and Measurement, Journal of Medical Systems, Computer Methods and Programs in Biomedicine and IEEE Reviews in Biomedical Engineering.
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.