Neeraj Dhungel

1.2k total citations
10 papers, 621 citations indexed

About

Neeraj Dhungel is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Neeraj Dhungel has authored 10 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Neeraj Dhungel's work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Advanced Neural Network Applications (3 papers). Neeraj Dhungel is often cited by papers focused on AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Advanced Neural Network Applications (3 papers). Neeraj Dhungel collaborates with scholars based in Australia, Canada and Portugal. Neeraj Dhungel's co-authors include Andrew P. Bradley, Gustavo Carneiro, Amir H. Abdi, Zhibin Liao, Robert Rohling, Christina Luong, Ken Gin, Hany Girgis, Teresa S.M. Tsang and Purang Abolmaesumi and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis and Adelaide Research & Scholarship (AR&S) (University of Adelaide).

In The Last Decade

Neeraj Dhungel

10 papers receiving 590 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Neeraj Dhungel Australia	8	517	421	184	106	81	10	621
Dina A. Ragab Egypt	8	502 1.0×	423 1.0×	135 0.7×	140 1.3×	49 0.6×	9	648
Quande Liu Hong Kong	8	439 0.8×	384 0.9×	277 1.5×	83 0.8×	40 0.5×	10	753
Yaozhong Luo China	6	394 0.8×	326 0.8×	206 1.1×	91 0.9×	34 0.4×	7	536
Wenqing Sun China	7	276 0.5×	296 0.7×	97 0.5×	56 0.5×	145 1.8×	23	501
Sarfaraz Hussein United States	7	206 0.4×	248 0.6×	79 0.4×	44 0.4×	64 0.8×	12	458
Shahzad Akbar Pakistan	16	227 0.4×	474 1.1×	318 1.7×	205 1.9×	26 0.3×	49	772
Hamid Zouaki Morocco	6	353 0.7×	269 0.6×	108 0.6×	70 0.7×	42 0.5×	18	489
Jie-Zhi Cheng Taiwan	6	325 0.6×	393 0.9×	148 0.8×	48 0.5×	115 1.4×	6	620
Yaniv Bar Israel	3	238 0.5×	282 0.7×	139 0.8×	30 0.3×	80 1.0×	4	487
Enas M. F. El Houby Egypt	11	347 0.7×	270 0.6×	111 0.6×	73 0.7×	32 0.4×	17	509

Countries citing papers authored by Neeraj Dhungel

Since Specialization

Citations

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

Fields of papers citing papers by Neeraj Dhungel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Neeraj Dhungel. 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 Neeraj Dhungel. The network helps show where Neeraj Dhungel may publish in the future.

Co-authorship network of co-authors of Neeraj Dhungel

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

All Works

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10 of 10 papers shown

Liao, Zhibin, Amir H. Abdi, Hany Girgis, et al.. (2019). Designing lightweight deep learning models for echocardiography view classification. 14–14. 17 indexed citations

Liao, Zhibin, Christina Luong, Hany Girgis, et al.. (2018). Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss. IEEE Transactions on Medical Imaging. 38(8). 1821–1832. 58 indexed citations

Dhungel, Neeraj, Gustavo Carneiro, & Andrew P. Bradley. (2017). A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical Image Analysis. 37. 114–128. 244 indexed citations

Min, Hang, Shekhar S. Chandra, Neeraj Dhungel, ‪Stuart Crozier‬, & Andrew P. Bradley. (2017). Multi-scale mass segmentation for mammograms via cascaded random forests. 113–117. 13 indexed citations

Dhungel, Neeraj, Gustavo Carneiro, & Andrew P. Bradley. (2017). Fully automated classification of mammograms using deep residual neural networks. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 310–314. 59 indexed citations

Cardoso, Jaime S., et al.. (2017). Mass segmentation in mammograms: A cross-sensor comparison of deep and tailored features. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1737–1741. 5 indexed citations

Dhungel, Neeraj. (2016). Automated detection, segmentation and classification of masses from mammograms using deep learning. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1 indexed citations

Dhungel, Neeraj, Gustavo Carneiro, & Andrew P. Bradley. (2015). Deep structured learning for mass segmentation from mammograms. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 2950–2954. 44 indexed citations

Dhungel, Neeraj, Gustavo Carneiro, & Andrew P. Bradley. (2015). Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1–8. 159 indexed citations

10.

Dhungel, Neeraj, Gustavo Carneiro, & Andrew P. Bradley. (2015). Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 760–763. 21 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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