L.S. Goodenday

649 citations
21 papers · 427 · h-index 9

Impact in

Papers in

L.S. Goodenday

21 papers receiving 379 citations

Peers

L.S. Goodenday
Comparison fields: 5 of 78
  • Health Information Management 48
  • Artificial Intelligence 177
  • Cardiology and Cardiovascular Medicine 116
  • Radiology, Nuclear Medicine and Imaging 86
  • Biophysics 15
Replace Mohammad Hossein Zangooei with:
Mohammad Hossein Zangooei Iran
Manuel Gomes Portugal
Rod Hose United Kingdom
M. Mostafizur Rahman United Kingdom
Wenqi Shi United States
Seral Şahan Türkiye
Yanlin Tan China
Thibault Helleputte Belgium
Po-Yen Wu United States
G. William Moore United States
L.S. Goodenday relative to Mohammad Hossein Zangooei Iran Mohammad Hossein Zangooei's profile →
Citations per field
00.5×1.5×2.5×
Mohammad Hossein Zangooei · 1×
Citations per year

Countries citing papers authored by L.S. Goodenday

Since Specialization
Citations

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

Fields of papers citing papers by L.S. Goodenday

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by L.S. Goodenday. 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 L.S. Goodenday. The network helps show where L.S. Goodenday may publish in the future.

Co-authors

The 19 scholars most cited alongside L.S. Goodenday, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with L.S. Goodenday Line = papers co-authored together L.S. Goodenday links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2001171
2 198799
3 197029
4 200221
5 199121
6 200014
7 197114
8 199411
9 200210
10 19968
11 19907
12 19896
13 19845
14
Scintigraphic detection of ventricular aneurysm with thallium-201.
19833
15 19932
16 19681
17 20031
18 19881
19 20021
20 20031

About L.S. Goodenday

L.S. Goodenday is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Molecular Biology, having authored 21 papers that have together received 427 indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (9 papers), Cardiac Imaging and Diagnostics (5 papers), Neural Networks and Applications (4 papers), Fuzzy Logic and Control Systems (3 papers), Medical Imaging Techniques and Applications (2 papers), Data Mining Algorithms and Applications (2 papers), Engineering Diagnostics and Reliability (2 papers) and Williams Syndrome Research (1 paper). The work is most often cited by research in Health Information Management (48 citations), Artificial Intelligence (177 citations), Cardiology and Cardiovascular Medicine (116 citations), Radiology, Nuclear Medicine and Imaging (86 citations) and Biophysics (15 citations). L.S. Goodenday has collaborated with scholars based in United States and Poland. Frequent co-authors include Krzysztof J. Cios, Marek R. Ogiela, Lukasz Kurgan, Ryszard Tadeusiewicz, Richard F. Leighton, Gary J. Muswick, William A. Yasnoff, Ik‐Soo Shin, Gilbert S. Gordan and Ellen Brown. Their work appears in journals such as Artificial Intelligence in Medicine, Annals of Internal Medicine, Blood, Academic Medicine and Computer.

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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