Ehsan Kozegar

573 total citations
26 papers, 421 citations indexed

About

Ehsan Kozegar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ehsan Kozegar has authored 26 papers receiving a total of 421 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ehsan Kozegar's work include AI in cancer detection (10 papers), Bladder and Urothelial Cancer Treatments (4 papers) and Brain Tumor Detection and Classification (3 papers). Ehsan Kozegar is often cited by papers focused on AI in cancer detection (10 papers), Bladder and Urothelial Cancer Treatments (4 papers) and Brain Tumor Detection and Classification (3 papers). Ehsan Kozegar collaborates with scholars based in Iran, Netherlands and United States. Ehsan Kozegar's co-authors include Mohsen Soryani, Ezzatollah Askari Asli‐Ardeh, Tao Tan, Hamid Behnam, Behrouz Minaei‐Bidgoli, Reyhaneh Loni, Inês Domingues, Zahra Pooranian, Hamid Hassanpour and Mohammad Shojafar and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Ehsan Kozegar

24 papers receiving 404 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ehsan Kozegar Iran 10 194 156 131 125 49 26 421
Mighty Abra Ayidzoe Ghana 7 141 0.7× 69 0.4× 131 1.0× 77 0.6× 25 0.5× 17 422
Prabhpreet Kaur India 12 178 0.9× 185 1.2× 158 1.2× 51 0.4× 27 0.6× 43 501
Patrick Kwabena Mensah Ghana 7 119 0.6× 64 0.4× 117 0.9× 76 0.6× 24 0.5× 27 371
Mahesh Gour India 9 272 1.4× 246 1.6× 168 1.3× 395 3.2× 131 2.7× 11 801
Linh T. Duong Vietnam 9 94 0.5× 148 0.9× 52 0.4× 76 0.6× 30 0.6× 16 402
Karim Gasmi Saudi Arabia 11 119 0.6× 72 0.5× 92 0.7× 74 0.6× 42 0.9× 34 345
Aboul Ella Hassenian Egypt 6 109 0.6× 67 0.4× 76 0.6× 118 0.9× 67 1.4× 7 315
Simón Orozco-Arias Colombia 14 132 0.7× 85 0.5× 103 0.8× 199 1.6× 13 0.3× 39 588
Xiangyu Lǚ China 9 105 0.5× 47 0.3× 65 0.5× 123 1.0× 32 0.7× 32 339
Saliha Zahoor Pakistan 5 180 0.9× 150 1.0× 145 1.1× 38 0.3× 20 0.4× 6 437

Countries citing papers authored by Ehsan Kozegar

Since Specialization
Citations

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

Fields of papers citing papers by Ehsan Kozegar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ehsan Kozegar

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

All Works

20 of 20 papers shown
1.
Behnam, Hamid, et al.. (2025). A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net. Computers in Biology and Medicine. 188. 109829–109829. 1 indexed citations
2.
Soryani, Mohsen, et al.. (2024). Mass segmentation in automated breast ultrasound using an enhanced attentive UNet. Expert Systems with Applications. 245. 123095–123095. 11 indexed citations
3.
Kozegar, Ehsan, et al.. (2024). Mass detection in automated three dimensional breast ultrasound using cascaded convolutional neural networks. Physica Medica. 124. 103433–103433. 1 indexed citations
4.
Vahidi, Javad, et al.. (2024). An end-to-end multi-task deep learning framework for bronchoscopy image classification. Multimedia Systems. 30(6).
5.
Xu, Xiayu, Leyuan Fang, Ehsan Kozegar, et al.. (2023). Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention. Frontiers in Medicine. 10. 1038534–1038534. 4 indexed citations
6.
Kozegar, Ehsan, et al.. (2022). Mass detection in automated 3-D breast ultrasound using a patch Bi-ConvLSTM network. Ultrasonics. 129. 106891–106891. 11 indexed citations
7.
Kozegar, Ehsan. (2021). Cystoscopic image classification by an ensemble of VGG-nets. International journal of nonlinear analysis and applications. 12(1). 693–700. 2 indexed citations
8.
Asli‐Ardeh, Ezzatollah Askari, et al.. (2021). Citrus pests classification using an ensemble of deep learning models. Computers and Electronics in Agriculture. 186. 106192–106192. 88 indexed citations
9.
Hassanpour, Hamid, et al.. (2020). Cystoscopic Image Classification Based on Combining MLP and GA. International journal of nonlinear analysis and applications. 11(1). 93–105. 9 indexed citations
10.
Kozegar, Ehsan, et al.. (2020). Pain Facial Expression Recognition from Video Sequences Using Spatio-temporal Local Binary Patterns and Tracking Fiducial Points. International Journal of Engineering. 33(6). 1 indexed citations
11.
Hassanpour, Hamid, et al.. (2019). Cystoscopy Image Classification Using Deep Convolutional Neural Networks. International journal of nonlinear analysis and applications. 10(1). 193–215. 2 indexed citations
12.
Kozegar, Ehsan, et al.. (2019). Training Feed-forward Neural Networks using Asexual Reproduction Optimization (ARO) Algorithm. 809–812. 1 indexed citations
13.
Kozegar, Ehsan, et al.. (2019). Computer aided detection in automated 3-D breast ultrasound images: a survey. Artificial Intelligence Review. 53(3). 1919–1941. 31 indexed citations
14.
Asli‐Ardeh, Ezzatollah Askari, et al.. (2019). Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K‐means. Food Science & Nutrition. 7(12). 3922–3930. 56 indexed citations
15.
Hassanpour, Hamid, et al.. (2019). An Overview of Face Detection Methods in Angular Positions. 12. 484–491. 2 indexed citations
16.
Kozegar, Ehsan, et al.. (2019). Applying Product Line Engineering Concepts to Deep Neural Networks. 72–77. 4 indexed citations
17.
Kozegar, Ehsan, et al.. (2017). Determining Mass Boundary in 3D Automated Breast Ultrasound Images Using a Deformable Model. 10(2). 16–26.
18.
Kozegar, Ehsan, et al.. (2017). Breast cancer detection in automated 3D breast ultrasound using iso-contours and cascaded RUSBoosts. Ultrasonics. 79. 68–80. 35 indexed citations
19.
Kozegar, Ehsan & Mohsen Soryani. (2017). A cost-sensitive Bayesian combiner for reducing false positives in mammographic mass detection. Biomedizinische Technik/Biomedical Engineering. 0(0). 39–52. 1 indexed citations
20.
Kozegar, Ehsan, Mohsen Soryani, Behrouz Minaei‐Bidgoli, & Inês Domingues. (2013). Assessment of a novel mass detection algorithm in mammograms. Journal of Cancer Research and Therapeutics. 9(4). 592–592. 53 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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