Morteza Heidari
- Radiology, Nuclear Medicine and Imaging top 2%
- Artificial Intelligence top 5%
- Pulmonary and Respiratory Medicine
- Computer Vision and Pattern Recognition top 10%
- Health Informatics top 2%
- Co-authors
- Abolfazl Zargari KhuzaniBin ZhengGopichandh DanalaSeyedehnafiseh MirniaharikandeheiYuchen QiuS. Ali ShariatiHong LiuAlan B. Hollingsworth
- Topics
- Radiomics and Machine Learning in Medical Imaging (17 papers)AI in cancer detection (17 papers)Digital Radiography and Breast Imaging (9 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsIEEE Transactions on Biomedical Engineering
- Partner nations
- United StatesIranCanada
In The Last Decade
Morteza Heidari
33 papers receiving 735 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Radiology, Nuclear Medicine and Imaging 562
- Artificial Intelligence 429
- Pulmonary and Respiratory Medicine 132
- Computer Vision and Pattern Recognition 132
- Health Informatics 66
Countries citing papers authored by Morteza Heidari
This map shows the geographic impact of Morteza Heidari'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 Morteza Heidari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Morteza Heidari more than expected).
Fields of papers citing papers by Morteza Heidari
This network shows the impact of papers produced by Morteza Heidari. 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 Morteza Heidari. The network helps show where Morteza Heidari may publish in the future.
Co-authorship network of co-authors of Morteza Heidari
This figure shows the co-authorship network connecting the top 25 collaborators of Morteza Heidari. A scholar is included among the top collaborators of Morteza Heidari 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 Morteza Heidari. Morteza Heidari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 37 | |
| 3 | 19 | |
| 4 | 124 | |
| 5 | 5 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithmsbreakdown → | 290 |
| 9 | 2 | |
| 10 | 29 | |
| 11 | 11 | |
| 12 | 5 | |
| 13 | 15 | |
| 14 | 26 | |
| 15 | 58 | |
| 16 | 62 | |
| 17 | 1 | |
| 18 | 3 | |
| 19 | 1 | |
| 20 | 2 |
About Morteza Heidari
Morteza Heidari is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 35 papers that have together received 759 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (17 papers) and Digital Radiography and Breast Imaging (9 papers). The work is most often cited by research in Health Informatics (66 citations), Radiology, Nuclear Medicine and Imaging (562 citations) and Artificial Intelligence (429 citations). Morteza Heidari has collaborated with scholars based in United States, Iran and Canada. Frequent co-authors include Abolfazl Zargari Khuzani, Bin Zheng, Gopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Yuchen Qiu, S. Ali Shariati, Hong Liu, Alan B. Hollingsworth, Bhavika Patel and S. Lakshmivarahan. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on 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.