Michael J. Horry
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- COVID-19 diagnosis using AI 7
- Radiomics and Machine Learning in Medical Imaging 4
-
- Lung Cancer Diagnosis and Treatment 3
- Phonocardiography and Auscultation Techniques 1
- Co-authors
- Subrata Chakraborty (7 shared papers)Biswajeet Pradhan (6 shared papers)Manoranjan Paul (5 shared papers)Anwaar Ulhaq (2 shared papers)Nagesh Shukla (1 shared paper)Manas Saha (1 shared paper)Mansour Almazroui (1 shared paper)Prabal Datta Barua (2 shared papers)
- Journals
- IEEE Access (2 papers)Sensors (1 paper)Mathematical Biosciences & Engineering (1 paper)Computers in Biology and Medicine (1 paper)Earth Systems and Environment (1 paper)
- Partner nations
- AustraliaMalaysiaSaudi Arabia
In The Last Decade
Michael J. Horry
8 papers receiving 379 citations
Peers
Comparison fields: 5 of 62
- Health Informatics 55
- Radiology, Nuclear Medicine and Imaging 316
- Artificial Intelligence 201
- Critical Care and Intensive Care Medicine 25
- General Dentistry 7
Countries citing papers authored by Michael J. Horry
This map shows the geographic impact of Michael J. Horry'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 Michael J. Horry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Horry more than expected).
Fields of papers citing papers by Michael J. Horry
This network shows the impact of papers produced by Michael J. Horry. 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 Michael J. Horry. The network helps show where Michael J. Horry may publish in the future.
Co-authors
The 15 scholars most cited alongside Michael J. Horry, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 326 | |
| 2 | 2022 | 19 | |
| 3 | 2023 | 14 | |
| 4 | 2021 | 12 | |
| 5 | 2023 | 8 | |
| 6 | 2021 | 4 | |
| 7 | 2021 | 3 | |
| 8 | 2023 | 2 |
About Michael J. Horry
Michael J. Horry is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Health Informatics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 388 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Lung Cancer Diagnosis and Treatment (3 papers), Artificial Intelligence in Healthcare and Education (2 papers), Automated Road and Building Extraction (1 paper), Remote-Sensing Image Classification (1 paper), Phonocardiography and Auscultation Techniques (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Health Informatics (55 citations), Radiology, Nuclear Medicine and Imaging (316 citations), Artificial Intelligence (201 citations), Critical Care and Intensive Care Medicine (25 citations) and General Dentistry (7 citations). Michael J. Horry has collaborated with scholars based in Australia, Malaysia and Saudi Arabia. Frequent co-authors include Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Anwaar Ulhaq, Nagesh Shukla, Manas Saha, Mansour Almazroui, Prabal Datta Barua, Jing Zhu and Hui Wen Loh. Their work appears in journals such as IEEE Access, Sensors, Mathematical Biosciences & Engineering, Computers in Biology and Medicine and Earth Systems and Environment.
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.