Alfonso Rojas‐Domínguez

941 citations
32 papers · 653 indexed · h-index 12
Topics
AI in cancer detection (8 papers)Image Retrieval and Classification Techniques (4 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)

In The Last Decade

Alfonso Rojas‐Domínguez

30 papers receiving 613 citations

Peers

Alfonso Rojas‐Domínguez
Comparison fields: 5 of 113
  • Artificial Intelligence 368
  • Computer Vision and Pattern Recognition 244
  • Radiology, Nuclear Medicine and Imaging 133
  • Control and Systems Engineering 84
  • Molecular Biology 82
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Countries citing papers authored by Alfonso Rojas‐Domínguez

Since Specialization
Citations

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

Fields of papers citing papers by Alfonso Rojas‐Domínguez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alfonso Rojas‐Domínguez. 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 Alfonso Rojas‐Domínguez. The network helps show where Alfonso Rojas‐Domínguez may publish in the future.

Co-authorship network of co-authors of Alfonso Rojas‐Domínguez

This figure shows the co-authorship network connecting the top 25 collaborators of Alfonso Rojas‐Domínguez. A scholar is included among the top collaborators of Alfonso Rojas‐Domínguez 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 Alfonso Rojas‐Domínguez. Alfonso Rojas‐Domínguez 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
#WorkIndexed citations
1 0
2 1
3 9
4 3
5 2
6 14
7 1
8 6
9 67
10 5
11 3
12 2
13 5
14 39
15 92
16 95
17
Detection of masses in mammograms using enhanced multilevel-thresholding segmentation and region selection based on rank
5
18 38
19 16
20 13

About Alfonso Rojas‐Domínguez

Alfonso Rojas‐Domínguez is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Modeling and Simulation, having authored 32 papers that have together received 653 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Image Retrieval and Classification Techniques (4 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (244 citations), Artificial Intelligence (368 citations) and Radiology, Nuclear Medicine and Imaging (133 citations). Alfonso Rojas‐Domínguez has collaborated with scholars based in Mexico, United Kingdom and United States. Frequent co-authors include Asoke K. Nandi, Martín Carpio, Héctor José Puga Soberanes, Héctor J. Fraire-Huacuja, Gabriel Corkidi, Elena López-Aguilera, Jordi Casademont, Josep Cotrina, Francisco Barceló-Arroyo and Horacio Rostro‐González. Their work appears in journals such as IEEE Access, BMC Bioinformatics and Pattern Recognition.

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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