Yoo Na Hwang

409 total citations
22 papers, 320 citations indexed

About

Yoo Na Hwang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Yoo Na Hwang has authored 22 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Yoo Na Hwang's work include Medical Image Segmentation Techniques (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Neural Networks and Applications (4 papers). Yoo Na Hwang is often cited by papers focused on Medical Image Segmentation Techniques (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Neural Networks and Applications (4 papers). Yoo Na Hwang collaborates with scholars based in South Korea, Taiwan and United States. Yoo Na Hwang's co-authors include Sung Min Kim, Sen Deng, Ju Hwan Lee, Yuan Jiang, Eun‐Seok Shin, Jae Hoon Jeong, Sung Min Kim, Sung‐Min Kim, Sang‐goo Lee and Ga‐Young Kim and has published in prestigious journals such as International Journal of Heat and Mass Transfer, IEEE Transactions on Biomedical Engineering and BioMed Research International.

In The Last Decade

Yoo Na Hwang

21 papers receiving 309 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yoo Na Hwang South Korea 10 82 71 55 54 53 22 320
Jong‐Il Park South Korea 14 22 0.3× 67 0.9× 54 1.0× 15 0.3× 32 0.6× 63 577
Hongjian Shi China 11 39 0.5× 67 0.9× 91 1.7× 11 0.2× 5 0.1× 47 481
Xiaojie Duan China 11 30 0.4× 113 1.6× 86 1.6× 88 1.6× 9 0.2× 45 359
André Kubagawa Sato Brazil 11 18 0.2× 47 0.7× 42 0.8× 60 1.1× 5 0.1× 51 397
Waldemar T. Smolik Poland 11 26 0.3× 124 1.7× 79 1.4× 147 2.7× 16 0.3× 49 449
Guanyu Zhou China 16 285 3.5× 129 1.8× 44 0.8× 146 2.7× 6 0.1× 57 902
Guozheng Yan China 8 20 0.2× 17 0.2× 162 2.9× 77 1.4× 115 2.2× 78 353
Yongli Xu China 11 109 1.3× 20 0.3× 15 0.3× 8 0.1× 13 0.2× 52 355
Marie Willemet United Kingdom 12 98 1.2× 16 0.2× 139 2.5× 12 0.2× 9 0.2× 16 551

Countries citing papers authored by Yoo Na Hwang

Since Specialization
Citations

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

Fields of papers citing papers by Yoo Na Hwang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yoo Na Hwang. 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 Yoo Na Hwang. The network helps show where Yoo Na Hwang may publish in the future.

Co-authorship network of co-authors of Yoo Na Hwang

This figure shows the co-authorship network connecting the top 25 collaborators of Yoo Na Hwang. A scholar is included among the top collaborators of Yoo Na Hwang 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 Yoo Na Hwang. Yoo Na Hwang 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.
2.
Kwak, Soobin, et al.. (2025). A Convergent Fourth-Order Finite Difference Scheme for the Black–Scholes Equation. Computational Economics.
3.
Lee, Sang‐goo, et al.. (2023). Adaptive undersampling and short clip-based two-stream CNN-LSTM model for surgical phase recognition on cholecystectomy videos. Biomedical Signal Processing and Control. 88. 105637–105637. 5 indexed citations
4.
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Hwang, Yoo Na, et al.. (2021). A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two‐Stage Approach. BioMed Research International. 2021(1). 5562801–5562801. 10 indexed citations
7.
Hwang, Yoo Na, et al.. (2019). Automated classification of dense calcium tissues in gray-scale intravascular ultrasound images using a deep belief network. BMC Medical Imaging. 19(1). 103–103. 10 indexed citations
8.
Lee, Ju Hwan, et al.. (2018). A novel intensity-based multi-level classification approach for coronary plaque characterization in intravascular ultrasound images. BioMedical Engineering OnLine. 17(S2). 151–151. 13 indexed citations
9.
Lee, Ju Hwan, et al.. (2018). Segmentation of the lumen and media‐adventitial borders in intravascular ultrasound images using a geometric deformable model. IET Image Processing. 12(10). 1881–1891. 15 indexed citations
10.
Lee, Ju Hwan, et al.. (2017). Analysis of Cardiovascular Tissue Components for the Diagnosis of Coronary Vulnerable Plaque from Intravascular Ultrasound Images. Journal of Healthcare Engineering. 2017. 1–7. 2 indexed citations
11.
Hwang, Yoo Na, et al.. (2017). Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier. Computer Methods and Programs in Biomedicine. 153. 83–92. 17 indexed citations
12.
Lee, Ju Hwan, et al.. (2015). Diagnosis of Osteoporosis by Quantification of Trabecular Microarchitectures from Hip Radiographs Using Artificial Neural Networks. Journal of Computational and Theoretical Nanoscience. 12(7). 1115–1120. 5 indexed citations
13.
Hwang, Yoo Na, et al.. (2015). Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network. Bio-Medical Materials and Engineering. 26(1_suppl). S1599–611. 50 indexed citations
14.
Lee, Ju Hwan, et al.. (2015). Classification of Osteoporosis by Extracting the Microarchitectural Properties of Trabecular Bone from DXA Scans Based on Thresholding Technique. Journal of Medical Imaging and Health Informatics. 5(8). 1782–1789. 3 indexed citations
15.
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Hwang, Yoo Na, et al.. (2008). Applying Neural Networks to the Solution of the Inverse Heat Conduction Problem in a Gun Barrel. Journal of Pressure Vessel Technology. 130(3). 6 indexed citations
17.
Deng, Sen & Yoo Na Hwang. (2007). Solving the Temperature Distribution Field in Nonlinear Heat Conduction Problems Using the Hopfield Neural Network. Numerical Heat Transfer Part B Fundamentals. 51(4). 375–389. 12 indexed citations
18.
Deng, Sen & Yoo Na Hwang. (2007). Solution of inverse heat conduction problems using Kalman filter-enhanced Bayesian back propagation neural network data fusion. International Journal of Heat and Mass Transfer. 50(11-12). 2089–2100. 35 indexed citations
19.
Hwang, Yoo Na, et al.. (2006). Applying neural networks to the solution of forward and inverse heat conduction problems. International Journal of Heat and Mass Transfer. 49(25-26). 4732–4750. 104 indexed citations
20.
Deng, Sen & Yoo Na Hwang. (2006). Applying the Hopfield Neural Network to the Solution of the Temperature Distribution Field in Heat Conduction Problems. Numerical Heat Transfer Part B Fundamentals. 50(6). 535–559. 5 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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