Hemat Allam

9 papers receiving 412 citations

Hit Papers

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review 2017 · 274 citations
274201720262020202350100150200250

Peers

Hemat Allam
Comparison fields: 5 of 78
  • Health Informatics 17
  • Radiology, Nuclear Medicine and Imaging 206
  • Artificial Intelligence 215
  • Neurology 54
  • Health Information Management 23
Replace Ting Cai with:
Ting Cai China
Zhuo He China
Enrico Pellegrini United Kingdom
Monika Turk Slovenia
Kyle Hasenstab United States
Trygve Eftestøl Norway
Benoît Magnin France
Vaanathi Sundaresan United Kingdom
Tara Retson United States
Roy Harnish United States
Hemat Allam relative to Ting Cai China Ting Cai's profile →
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Citations per year

Countries citing papers authored by Hemat Allam

Since Specialization
Citations

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

Fields of papers citing papers by Hemat Allam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 10 scholars most cited alongside Hemat Allam, 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 Hemat Allam Line = papers co-authored together Hemat Allam links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1
Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review
Hit paper breakdown →
2017274
2 201458
3 200838
4 201836
5 201313
6 20144
7 20192
8 20251
9 20131
10 20160

About Hemat Allam

Hemat Allam is a scholar working on Complementary and alternative medicine, Radiology, Nuclear Medicine and Imaging, Cell Biology, Cognitive Neuroscience and Cardiology and Cardiovascular Medicine, having authored 10 papers that have together received 427 indexed citations. Recurring topics across this work include Acupuncture Treatment Research Studies (4 papers), Cardiac Arrhythmias and Treatments (2 papers), Myofascial pain diagnosis and treatment (2 papers), Laser Applications in Dentistry and Medicine (2 papers), Cardiovascular Issues in Pregnancy (2 papers), Palliative Care and End-of-Life Issues (2 papers), Cardiac Structural Anomalies and Repair (2 papers) and Child Nutrition and Feeding Issues (1 paper). The work is most often cited by research in Health Informatics (17 citations), Radiology, Nuclear Medicine and Imaging (206 citations), Artificial Intelligence (215 citations), Neurology (54 citations) and Health Information Management (23 citations). Hemat Allam has collaborated with scholars based in Egypt, United Kingdom and South Africa. Frequent co-authors include Enas M. F. El Houby, Nisreen I. R. Yassin, Shaimaa Omran, Ahmed Hassouna, Eman A. Elghoroury, Nagwa Mohamed, Damien Ridge, Vikash Sewram, Marie Polley and Samir Abdelrahman. Their work appears in journals such as Interactive Cardiovascular and Thoracic Surgery, Computer Methods and Programs in Biomedicine, The Journal of Alternative and Complementary Medicine, BMC Women s Health and Medical Acupuncture.

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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