Muhammad Ishfaq

5.3k citations
152 papers · 3.9k · 1 hit paper · h-index 38

Impact in

Papers in

Muhammad Ishfaq

142 papers receiving 3.8k citations

Muhammad Ishfaq's Hit Papers

A deep learning approach for brain tumor classification using MRI images 2022 · 168 citations
1680+1+2Years since publication50100150

Peers

Muhammad Ishfaq
Comparison fields: 5 of 153
  • Microbiology 507
  • Molecular Medicine 305
  • Animal Science and Zoology 439
  • Pharmacology 288
  • Neurology 211
Replace Zhengtao Yang with:
Zhengtao Yang China
Seung‐Chun Park South Korea
Marc Maresca France
Yuanliang Hu China
Yuanfeng Zou China
Lizi Yin China
Jiaguo Liu China
Yong‐Suk Jang South Korea
Priyia Pusparajah Malaysia
Xiaoyun Liu China
Muhammad Ishfaq relative to Zhengtao Yang China Zhengtao Yang's profile →
Citations per field
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Citations per year

Countries citing papers authored by Muhammad Ishfaq

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Ishfaq

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 152 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A deep learning approach for brain tumor classification using MRI images
Hit paper breakdown →
2022168
2 2019116
3 2017112
4 201997
5 201787
6 201986
7 202282
8 201781
9 201979
10 201878
11 201972
12 202271
13 201771
14 202066
15 202062
16 202160
17 201759
18 202058
19 202055
20 201854

About Muhammad Ishfaq

Muhammad Ishfaq is a scholar working on Molecular Biology, Microbiology, Epidemiology, Immunology and Plant Science, having authored 152 papers that have together received 3.9k indexed citations. Recurring topics across this work include Microbial infections and disease research (29 papers), Aquaculture disease management and microbiota (18 papers), Genomics, phytochemicals, and oxidative stress (15 papers), Antibiotic Resistance in Bacteria (11 papers), Mycobacterium research and diagnosis (10 papers), Mycotoxins in Agriculture and Food (9 papers), Curcumin's Biomedical Applications (9 papers) and Toxoplasma gondii Research Studies (9 papers). The work is most often cited by research in Microbiology (507 citations), Molecular Medicine (305 citations), Animal Science and Zoology (439 citations), Pharmacology (288 citations) and Neurology (211 citations). Muhammad Ishfaq has collaborated with scholars based in China, Pakistan and Saudi Arabia. Frequent co-authors include Jichang Li, Xiuying Zhang, Xiaoqi Sun, Chunli Chen, Sihong Li, Syed Waqas Ali Shah, Zhiyong Wu, Jian Wang, Rui Li and Wanying Hu. Their work appears in journals such as Poultry Science, Frontiers in Pharmacology, Ecotoxicology and Environmental Safety, Frontiers in Veterinary Science and Veterinary Research.

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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