Fahdi Kanavati
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 2%
- Oncology
- Pulmonary and Respiratory Medicine
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Masayuki TsunekiOsamu IizukaKei KatoKoji ArihiroSadanori TakeoKoji YamazakiGouji ToyokawaSeiya Momosaki
- Topics
- AI in cancer detection (14 papers)Radiomics and Machine Learning in Medical Imaging (10 papers)Colorectal Cancer Screening and Detection (8 papers)
- Partner nations
- JapanUnited KingdomUnited States
In The Last Decade
Fahdi Kanavati
19 papers receiving 761 citations
Hit Papers
Peers
Comparison fields: 5 of 81
- Radiology, Nuclear Medicine and Imaging 504
- Artificial Intelligence 500
- Oncology 218
- Pulmonary and Respiratory Medicine 168
- Computer Vision and Pattern Recognition 122
Countries citing papers authored by Fahdi Kanavati
This map shows the geographic impact of Fahdi Kanavati'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 Fahdi Kanavati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fahdi Kanavati more than expected).
Fields of papers citing papers by Fahdi Kanavati
This network shows the impact of papers produced by Fahdi Kanavati. 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 Fahdi Kanavati. The network helps show where Fahdi Kanavati may publish in the future.
Co-authorship network of co-authors of Fahdi Kanavati
This figure shows the co-authorship network connecting the top 25 collaborators of Fahdi Kanavati. A scholar is included among the top collaborators of Fahdi Kanavati based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fahdi Kanavati. Fahdi Kanavati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 7 | |
| 6 | 45 | |
| 7 | 7 | |
| 8 | 18 | |
| 9 | 13 | |
| 10 | 22 | |
| 11 | 7 | |
| 12 | 10 | |
| 13 | 39 | |
| 14 | 17 | |
| 15 | 3 | |
| 16 | 19 | |
| 17 | Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumoursbreakdown → | 271 |
| 18 | 155 | |
| 19 | 128 | |
| 20 | 10 |
About Fahdi Kanavati
Fahdi Kanavati is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine, having authored 20 papers that have together received 794 indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Colorectal Cancer Screening and Detection (8 papers). The work is most often cited by research in Health Informatics (51 citations), Radiology, Nuclear Medicine and Imaging (504 citations) and Artificial Intelligence (500 citations). Fahdi Kanavati has collaborated with scholars based in Japan, United Kingdom and United States. Frequent co-authors include Masayuki Tsuneki, Osamu Iizuka, Kei Kato, Koji Arihiro, Sadanori Takeo, Koji Yamazaki, Gouji Toyokawa, Seiya Momosaki, Fumihiro Shoji and Yuka Kozuma. Their work appears in journals such as Nature Communications, PLoS ONE and American Journal of Respiratory and Critical Care Medicine.
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.