Mohammed M. Abdelsamea
- Health Informatics top 2%
-
- Radiomics and Machine Learning in Medical Imaging 12
- COVID-19 diagnosis using AI 7
- Artificial Intelligence top 5%
- AI in cancer detection 18
- Anomaly Detection Techniques and Applications 3
- Machine Learning and ELM 2
-
- Medical Image Segmentation Techniques 8
- Digital Imaging for Blood Diseases 6
- Image Retrieval and Classification Techniques 5
- Neurology top 10%
- Co-authors
- Mohamed Medhat GaberAsmaa AbbasSotirios A. TsaftarisMassimo MinerviniGiorgio GneccoEmad A. RakhaMohammed ZidanAlaa Sagheer
- Journals
- SHILAP Revista de lepidopterología (2 papers)Scientific Reports (2 papers)Expert Systems with Applications (2 papers)
- Partner nations
- EgyptUnited KingdomItaly
In The Last Decade
Mohammed M. Abdelsamea
31 papers receiving 804 citations
Peers
Comparison fields: 5 of 123
- Health Informatics 50
- Radiology, Nuclear Medicine and Imaging 333
- Artificial Intelligence 425
- Computer Vision and Pattern Recognition 233
- Neurology 64
Countries citing papers authored by Mohammed M. Abdelsamea
This map shows the geographic impact of Mohammed M. Abdelsamea'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 Mohammed M. Abdelsamea with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed M. Abdelsamea more than expected).
Fields of papers citing papers by Mohammed M. Abdelsamea
This network shows the impact of papers produced by Mohammed M. Abdelsamea. 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 Mohammed M. Abdelsamea. The network helps show where Mohammed M. Abdelsamea may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mohammed M. Abdelsamea, 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 | 2025 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2023 | 4 | |
| 4 | 2023 | 0 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 28 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 9 | |
| 10 | 2022 | 40 | |
| 11 | 2022 | 40 | |
| 12 | 2021 | 58 | |
| 13 | 2019 | 122 | |
| 14 | 2018 | 23 | |
| 15 | 2017 | 0 | |
| 16 | 2016 | 6 | |
| 17 | 2015 | 10 | |
| 18 | 2014 | 48 | |
| 19 | 2014 | 3 | |
| 20 | 2013 | 19 |
About Mohammed M. Abdelsamea
Mohammed M. Abdelsamea is a scholar working on Artificial Intelligence, Health Informatics and Computer Vision and Pattern Recognition, having authored 35 papers that have together received 829 indexed citations. Recurring topics across this work include AI in cancer detection (18 papers), Radiomics and Machine Learning in Medical Imaging (12 papers), Medical Image Segmentation Techniques (8 papers), COVID-19 diagnosis using AI (7 papers), Digital Imaging for Blood Diseases (6 papers), Image Retrieval and Classification Techniques (5 papers), Anomaly Detection Techniques and Applications (3 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Health Informatics (50 citations), Radiology, Nuclear Medicine and Imaging (333 citations) and Artificial Intelligence (425 citations). Mohammed M. Abdelsamea has collaborated with scholars based in Egypt, United Kingdom and Italy. Frequent co-authors include Mohamed Medhat Gaber, Asmaa Abbas, Sotirios A. Tsaftaris, Massimo Minervini, Giorgio Gnecco, Emad A. Rakha, Mohammed Zidan, Alaa Sagheer, Ronnachai Jaroensri and Po-Hsuan Cameron Chen. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.
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.