Radu Badea

3.1k total citations
60 papers, 1.1k citations indexed

About

Radu Badea is a scholar working on Epidemiology, Hepatology and Artificial Intelligence. According to data from OpenAlex, Radu Badea has authored 60 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Epidemiology, 25 papers in Hepatology and 21 papers in Artificial Intelligence. Recurrent topics in Radu Badea's work include Liver Disease Diagnosis and Treatment (33 papers), AI in cancer detection (18 papers) and Hepatocellular Carcinoma Treatment and Prognosis (15 papers). Radu Badea is often cited by papers focused on Liver Disease Diagnosis and Treatment (33 papers), AI in cancer detection (18 papers) and Hepatocellular Carcinoma Treatment and Prognosis (15 papers). Radu Badea collaborates with scholars based in Romania, France and Italy. Radu Badea's co-authors include Monica Lupșor‐Platon, Horia Ştefănescu, Anca Maniu, Mircea Grigorescu, Sergiu Nedevschi, Zeno Spârchez, Bogdan Procopeț, Alexandru Șerban, Sorana C. Iancu and Diana Feier and has published in prestigious journals such as SHILAP Revista de lepidopterología, Hepatology and Journal of Hepatology.

In The Last Decade

Radu Badea

54 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Radu Badea Romania 16 814 667 200 152 134 60 1.1k
Monica Lupșor‐Platon Romania 24 1.5k 1.9× 1.1k 1.6× 348 1.7× 113 0.7× 235 1.8× 77 2.1k
Horia Ştefănescu Romania 24 1.7k 2.1× 1.5k 2.3× 204 1.0× 51 0.3× 425 3.2× 107 2.0k
Fankun Meng China 10 328 0.4× 308 0.5× 291 1.5× 108 0.7× 83 0.6× 30 737
Bong‐Wan Kim South Korea 22 320 0.4× 703 1.1× 53 0.3× 109 0.7× 605 4.5× 93 1.4k
Noboru Takata Japan 17 219 0.3× 128 0.2× 110 0.6× 94 0.6× 145 1.1× 76 1.1k
Shunsuke Imai Japan 20 510 0.6× 196 0.3× 36 0.2× 469 3.1× 169 1.3× 52 1.4k
Wenqi Shi United States 15 109 0.1× 145 0.2× 340 1.7× 225 1.5× 60 0.4× 79 954
Masahiko Sakai Japan 24 563 0.7× 448 0.7× 51 0.3× 130 0.9× 722 5.4× 118 1.9k
P.S. Zoumpoulis Greece 11 208 0.3× 155 0.2× 166 0.8× 105 0.7× 26 0.2× 29 403
Hong‐Ming Tsai Taiwan 11 111 0.1× 183 0.3× 189 0.9× 155 1.0× 185 1.4× 33 648

Countries citing papers authored by Radu Badea

Since Specialization
Citations

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

Fields of papers citing papers by Radu Badea

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Radu Badea

This figure shows the co-authorship network connecting the top 25 collaborators of Radu Badea. A scholar is included among the top collaborators of Radu Badea 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 Radu Badea. Radu Badea 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.
Nedevschi, Sergiu, et al.. (2023). Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques. Sensors. 23(5). 2520–2520. 17 indexed citations
2.
Nedevschi, Sergiu, et al.. (2023). Liver Tumor Segmentation From Computed Tomography Images Through Convolutional Neural Networks. 1–6. 1 indexed citations
3.
4.
5.
Radu, Corina, Iosif Bîrlescu, Tiberiu Mariţa, et al.. (2020). Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma. Biology. 9(11). 397–397. 6 indexed citations
6.
7.
Cantisani, Vito, Emanuele David, Maria Franca Meloni, et al.. (2015). Recall strategies for patients found to have a nodule in cirrhosis: is there still a role for CEUS?. Medical Ultrasonography. 17(4). 515–20. 14 indexed citations
8.
Lupșor‐Platon, Monica, Diana Feier, Horia Ştefănescu, et al.. (2015). Diagnostic Accuracy of Controlled Attenuation Parameter Measured by Transient Elastography for the Non-invasive Assessment of Liver Steatosis: a Prospective Study. Journal of Gastrointestinal and Liver Diseases. 24(1). 35–42. 39 indexed citations
9.
Lupșor‐Platon, Monica, Horia Ştefănescu, Daniel Mureșan, et al.. (2014). Noninvasive assessment of liver steatosis using ultrasound methods. Medical Ultrasonography. 16(3). 236–45. 51 indexed citations
10.
Crişan, Maria, Doiniţa Crişan, & Radu Badea. (2014). Thrombophlebitis of the lateral chest wall (Mondor′s disease). Indian Journal of Dermatology Venereology and Leprology. 80(1). 96–96. 5 indexed citations
11.
Arena, Umberto, Monica Lupșor‐Platon, Cristina Stasi, et al.. (2013). Liver stiffness is influenced by a standardized meal in patients with chronic hepatitis C virus at different stages of fibrotic evolution. Hepatology. 58(1). 65–72. 138 indexed citations
12.
Lupșor‐Platon, Monica, et al.. (2013). Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods. 4. 633–637. 1 indexed citations
13.
Nedevschi, Sergiu, et al.. (2012). The Role of the Superior Order GLCM in the Characterization and Recognition of the Liver Tumors from Ultrasound Images. SHILAP Revista de lepidopterología. 9 indexed citations
14.
Stoean, Cătălin, Ruxandra Stoean, Monica Lupșor‐Platon, Horia Ştefănescu, & Radu Badea. (2011). Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis. Computers in Biology and Medicine. 41(4). 238–246. 24 indexed citations
15.
Ştefănescu, Horia, Mircea Grigorescu, Monica Lupșor‐Platon, et al.. (2010). Spleen stiffness measurement using fibroscan for the noninvasive assessment of esophageal varices in liver cirrhosis patients. Journal of Gastroenterology and Hepatology. 26(1). 164–170. 160 indexed citations
16.
Stoean, Ruxandra, Cătălin Stoean, Monica Lupșor‐Platon, Horia Ştefănescu, & Radu Badea. (2010). Evolutionary conditional rules versus support vector machines weighted formulas for liver fibrosis degree prediction. 37(1). 43–54. 1 indexed citations
17.
Stoean, Ruxandra, Cătălin Stoean, Monica Lupșor‐Platon, Horia Ştefănescu, & Radu Badea. (2010). Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C. Artificial Intelligence in Medicine. 51(1). 53–65. 37 indexed citations
18.
Belciug, Smaranda, Monica Lupșor‐Platon, & Radu Badea. (2008). Features selection approach for non-invasive evaluation of liver fibrosis. Annals of the University of Craiova Mathematics and Computer Science Series. 35. 15–20. 5 indexed citations
19.
Lupșor‐Platon, Monica, Radu Badea, Sergiu Nedevschi, et al.. (2008). Detection of steatosis in chronic hepatitis C, based on the evaluation of the attenuation coefficient computed on the ultrasound image. Medical Ultrasonography. 10(1). 13–20. 3 indexed citations
20.
Badea, Radu, Mihai Socaciu, Monica Lupșor‐Platon, Ofelia Moșteanu, & Teodora Pop. (2007). Evaluating the liver tumors using three-dimensional ultrasonography. A pictorial essay.. PubMed. 16(1). 85–92. 10 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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