Alfonso Medela

619 citations
15 papers · 402 indexed · 1 hit paper · h-index 8
Topics
AI in cancer detection (5 papers)Head and Neck Cancer Studies (3 papers)Salivary Gland Tumors Diagnosis and Treatment (3 papers)
Journals
SHILAP Revista de lepidopterologíaIEEE AccessJournal of the American Academy of Dermatology
Partner nations
SpainItalyBelgium

In The Last Decade

Alfonso Medela

14 papers receiving 381 citations

Hit Papers

Few-Shot Learning approach for plant disease classificati...2020202620222024202050100150200

Peers

Alfonso Medela
Comparison fields: 5 of 74
  • Plant Science 225
  • Artificial Intelligence 81
  • Analytical Chemistry 67
  • Radiology, Nuclear Medicine and Imaging 53
  • Ecology 38
Replace Ulzii-Orshikh Dorj with:
Ulzii-Orshikh Dorj South Korea
Linh T. Duong Vietnam
S. M. Jaisakthi India
Fares Bougourzi France
Zeeshan Abbas South Korea
Ashish Semwal India
Jayant Jagtap India
Rafael Namías Argentina
Veysel Turk Türkiye
Zhencun Jiang China
Alfonso Medela relative to Ulzii-Orshikh Dorj South Korea Ulzii-Orshikh Dorj's profile →
Citations per field
00.5×1.5×1.8×
Ulzii-Orshikh Dorj · 1×
Citations per year

Countries citing papers authored by Alfonso Medela

Since Specialization
Citations

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

Fields of papers citing papers by Alfonso Medela

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alfonso Medela

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

All Works

15 of 15 papers shown
#WorkIndexed citations
1 0
2 2
3 13
4 3
5 1
6 21
7 13
8 4
9 1
10 15
11 14
12 20
13 4
14
Few-Shot Learning approach for plant disease classification using images taken in the fieldbreakdown →
248
15 43

About Alfonso Medela

Alfonso Medela is a scholar working on Health Informatics, Otorhinolaryngology and Dermatology, having authored 15 papers that have together received 402 indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), Head and Neck Cancer Studies (3 papers) and Salivary Gland Tumors Diagnosis and Treatment (3 papers). The work is most often cited by research in Health Informatics (17 citations), Analytical Chemistry (67 citations) and Plant Science (225 citations). Alfonso Medela has collaborated with scholars based in Spain, Italy and Belgium. Frequent co-authors include Artzai Picón, Arantza Bereciartúa-Pérez, Aitor Álvarez-Gila, Unai Irusta, Carlos M. Chiesa‐Estomba, Riccardo Cicchi, Cristina L. Saratxaga, Ben Glover, Roberto Bilbao and Ramón Grimalt. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Journal of the American Academy of Dermatology.

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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