Muhammad Rashid

880 citations
14 papers · 682 indexed · h-index 11
Topics
Cutaneous Melanoma Detection and Management (5 papers)AI in cancer detection (4 papers)Optical Coherence Tomography Applications (3 papers)

In The Last Decade

Muhammad Rashid

14 papers receiving 631 citations

Peers

Muhammad Rashid
Comparison fields: 5 of 82
  • Oncology 300
  • Artificial Intelligence 282
  • Computer Vision and Pattern Recognition 263
  • Radiology, Nuclear Medicine and Imaging 126
  • Gastroenterology 93
Replace Farhat Afza with:
Farhat Afza Pakistan
Umut Özkaya Türkiye
Adrián Colomer Spain
Joanna Jaworek-Korjakowska Poland
Bisser Raytchev Japan
Hiam Alquran Jordan
Euijoon Ahn Australia
Shafqat Ali Shad United States
Duc My Vo South Korea
Hamidullah Binol United States
Muhammad Rashid relative to Farhat Afza Pakistan Farhat Afza's profile →
Citations per field
00.5×1.5×1.9×
Farhat Afza · 1×
Citations per year

Countries citing papers authored by Muhammad Rashid

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Rashid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Muhammad Rashid

This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Rashid. A scholar is included among the top collaborators of Muhammad Rashid based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Muhammad Rashid. Muhammad Rashid is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
#WorkIndexed citations
1 1
2 4
3 117
4 22
5 22
6 62
7 121
8 58
9 90
10 10
11 3
12 67
13 21
14 84

About Muhammad Rashid

Muhammad Rashid is a scholar working on Computer Vision and Pattern Recognition, Gastroenterology and Media Technology, having authored 14 papers that have together received 682 indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (5 papers), AI in cancer detection (4 papers) and Optical Coherence Tomography Applications (3 papers). The work is most often cited by research in Gastroenterology (93 citations), Computer Vision and Pattern Recognition (263 citations) and Oncology (300 citations). Muhammad Rashid has collaborated with scholars based in Pakistan, Spain and United States. Frequent co-authors include Muhammad Attique Khan, Qaisar Abbas, Irene Fondón, Farhat Afza, Muhammad Sharif, Muhammad Sharif, Tanzila Saba, Kashif Javed, Tallha Akram and Mussarat Yasmin. Their work appears in journals such as Sustainability, Computer Methods and Programs in Biomedicine and Neural Computing and 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.

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