Assif Assad

41 papers receiving 698 citations

Assif Assad's Hit Papers

Breast cancer detection using deep learning: Datasets, methods, and challenges ahead 2022 · 210 citations
2100+1+2Years since publication50100150200

Peers

Assif Assad
Comparison fields: 5 of 116
  • Artificial Intelligence 308
  • Health Informatics 11
  • Neurology 58
  • Radiology, Nuclear Medicine and Imaging 126
  • Health Information Management 23
Replace Heba G. Mohamed with:
Heba G. Mohamed Saudi Arabia
Sunil L. Bangare India
Waleed M. Mohamed Egypt
Biswaranjan Acharya India
Hanaa Salem Egypt
V. Muthukumaran India
Puspanjali Mohapatra India
Salabat Khan Pakistan
Mahua Bhattacharya India
Mohamad Khairi Ishak Malaysia
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Citations per year

Countries citing papers authored by Assif Assad

Since Specialization
Citations

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

Fields of papers citing papers by Assif Assad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Assif Assad, 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 Assif Assad Line = papers co-authored together Assif Assad links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Hit paper breakdown →
2022210
2 2018100
3 202084
4 202435
5 202334
6 201931
7 202225
8 202321
9 202420
10 202219
11 202413
12 202513
13 202410
14 201710
15 20249
16 20239
17 20247
18 20256
19 20176
20 20236

About Assif Assad

Assif Assad is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, Molecular Biology and Computer Vision and Pattern Recognition, having authored 47 papers that have together received 719 indexed citations. Recurring topics across this work include AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Anomaly Detection Techniques and Applications (4 papers), Spectroscopy and Chemometric Analyses (4 papers), Smart Agriculture and AI (4 papers), Artificial Intelligence in Healthcare (3 papers), Computational Drug Discovery Methods (3 papers) and Digital Holography and Microscopy (3 papers). The work is most often cited by research in Artificial Intelligence (308 citations), Health Informatics (11 citations), Neurology (58 citations), Radiology, Nuclear Medicine and Imaging (126 citations) and Health Information Management (23 citations). Assif Assad has collaborated with scholars based in India, Qatar and Vietnam. Frequent co-authors include Kusum Deep, Nusrat Mohi Ud Din, Rayees Ahmad Dar, Hossein Moayedi, Shubham Gupta, Loke Kok Foong, Rakesh Kumar Meena, Madhu Jain, Sudeep Singh Sanga and Mandeep Singh. Their work appears in journals such as International Journal of Systems Assurance Engineering and Management, Mathematics and Computers in Simulation, Journal of Computational Biology, Engineering Applications of Artificial Intelligence and Engineering With Computers.

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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