I. El-Naqa

962 total citations
9 papers, 699 citations indexed

About

I. El-Naqa is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, I. El-Naqa has authored 9 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in I. El-Naqa's work include AI in cancer detection (5 papers), Image Retrieval and Classification Techniques (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). I. El-Naqa is often cited by papers focused on AI in cancer detection (5 papers), Image Retrieval and Classification Techniques (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). I. El-Naqa collaborates with scholars based in United States and Greece. I. El-Naqa's co-authors include Robert M. Nishikawa, Yongyi Yang, Miles N. Wernick, Yan Yang, Yongyi Yang, N.P. Galatsanos, Janet S. Rader, David G. Mutch, Perry W. Grigsby and Imran Zoberi and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging and Proceedings - International Conference on Image Processing.

In The Last Decade

I. El-Naqa

9 papers receiving 640 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
I. El-Naqa United States 7 381 376 172 108 54 9 699
Liyang Wei United States 10 349 0.9× 260 0.7× 196 1.1× 87 0.8× 33 0.6× 25 581
Alfonso Rojas‐Domínguez Mexico 12 368 1.0× 244 0.6× 133 0.8× 82 0.8× 36 0.7× 32 653
Marcello Salmeri Italy 14 483 1.3× 407 1.1× 217 1.3× 53 0.5× 143 2.6× 74 812
Mutawarra Hussain Pakistan 12 253 0.7× 259 0.7× 183 1.1× 57 0.5× 62 1.1× 22 520
Joan Martı́ Spain 13 533 1.4× 446 1.2× 321 1.9× 67 0.6× 59 1.1× 43 798
Abdul Majid Pakistan 14 217 0.6× 145 0.4× 179 1.0× 125 1.2× 70 1.3× 39 601
Yongyi Yang United States 4 648 1.7× 451 1.2× 360 2.1× 155 1.4× 79 1.5× 6 976
Datong Wei United States 8 342 0.9× 252 0.7× 209 1.2× 51 0.5× 41 0.8× 19 494
Matthias Elter Germany 11 406 1.1× 236 0.6× 232 1.3× 92 0.9× 32 0.6× 26 620
Chung‐Ming Wu Taiwan 5 194 0.5× 242 0.6× 163 0.9× 35 0.3× 70 1.3× 7 544

Countries citing papers authored by I. El-Naqa

Since Specialization
Citations

This map shows the geographic impact of I. El-Naqa'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 I. El-Naqa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites I. El-Naqa more than expected).

Fields of papers citing papers by I. El-Naqa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I. El-Naqa. 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 I. El-Naqa. The network helps show where I. El-Naqa may publish in the future.

Co-authorship network of co-authors of I. El-Naqa

This figure shows the co-authorship network connecting the top 25 collaborators of I. El-Naqa. A scholar is included among the top collaborators of I. El-Naqa 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 I. El-Naqa. I. El-Naqa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Mutch, David G., Janet S. Rader, Randall K. Gibb, et al.. (2006). Comparison of high-dose-rate and low-dose-rate brachytherapy in the treatment of endometrial carcinoma. International Journal of Radiation Oncology*Biology*Physics. 67(2). 480–484. 30 indexed citations
2.
El-Naqa, I., et al.. (2004). A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography. IEEE Transactions on Medical Imaging. 23(10). 1233–1244. 197 indexed citations
3.
Parikh, Parag J., Wei Lü, J Hubenschmidt, et al.. (2004). Conformal treatment planning using 4DCT can decrease ipsilateral lung dose and improve tumor coverage: A prospective 4DCT treatment planning study. International Journal of Radiation Oncology*Biology*Physics. 60(1). S286–S287. 3 indexed citations
4.
Parikh, Parag J., Wenfu Lu, J Hubenschmidt, et al.. (2004). Conformal treatment planning using 4DCT can decrease ipsilateral lung dose and improve tumor coverage: A prospective 4DCT treatment planning study. International Journal of Radiation Oncology*Biology*Physics. 60. S286–S287. 1 indexed citations
5.
Brankov, Jovan G., I. El-Naqa, Yongyi Yang, & Miles N. Wernick. (2004). Learning a nonlinear channelized observer for image quality assessment. 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515). 1280. 2526–2529. 9 indexed citations
6.
El-Naqa, I., Yongyi Yang, Miles N. Wernick, N.P. Galatsanos, & Robert M. Nishikawa. (2003). A support vector machine approach for detection of microcalcifications in mammograms. Proceedings - International Conference on Image Processing. 2. II–953. 8 indexed citations
7.
El-Naqa, I., Yongyi Yang, Miles N. Wernick, N.P. Galatsanos, & Robert M. Nishikawa. (2003). Support vector machine learning for detection of microcalcifications in mammograms. 201–204. 22 indexed citations
8.
El-Naqa, I., et al.. (2002). A support vector machine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging. 21(12). 1552–1563. 423 indexed citations
9.
El-Naqa, I., Miles N. Wernick, Yongyi Yang, & Nikolaos Galatsanos. (2002). Image retrieval based on similarity learning. 722–725. 6 indexed citations

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.

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