Ahmad Naeem

1.1k total citations · 1 hit paper
25 papers, 680 citations indexed

About

Ahmad Naeem is a scholar working on Artificial Intelligence, Oncology and Epidemiology. According to data from OpenAlex, Ahmad Naeem has authored 25 papers receiving a total of 680 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Oncology and 8 papers in Epidemiology. Recurrent topics in Ahmad Naeem's work include AI in cancer detection (9 papers), Cutaneous Melanoma Detection and Management (8 papers) and Nonmelanoma Skin Cancer Studies (7 papers). Ahmad Naeem is often cited by papers focused on AI in cancer detection (9 papers), Cutaneous Melanoma Detection and Management (8 papers) and Nonmelanoma Skin Cancer Studies (7 papers). Ahmad Naeem collaborates with scholars based in Pakistan, South Korea and United States. Ahmad Naeem's co-authors include Rizwan Ali Naqvi, Tayyaba Anees, Hassaan Malik, Woong-Kee Loh, Seung Won Lee, Adnan Abid, Adel Khelifi, Muhammad Shoaib Farooq, Jawad Tanveer and Taeg Keun Whangbo and has published in prestigious journals such as PLoS ONE, IEEE Access and Sensors.

In The Last Decade

Ahmad Naeem

20 papers receiving 649 citations

Hit Papers

DSCC_Net: Multi-Classific... 2023 2026 2024 2023 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images
    2023 105 citations Ahmad Naeem, Hassaan Malik et al. Cancers profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ahmad Naeem Pakistan 13 388 329 193 152 94 25 680
Nazia Hameed United Kingdom 9 276 0.7× 274 0.8× 124 0.6× 96 0.6× 79 0.8× 28 590
Manu Goyal United Kingdom 11 541 1.4× 425 1.3× 279 1.4× 216 1.4× 205 2.2× 18 1.1k
Md Shahin Ali Bangladesh 15 462 1.2× 248 0.8× 165 0.9× 187 1.2× 170 1.8× 25 900
Walaa Gouda Saudi Arabia 10 267 0.7× 209 0.6× 163 0.8× 91 0.6× 104 1.1× 16 501
Mohammad H. Jafari Canada 10 302 0.8× 337 1.0× 191 1.0× 115 0.8× 151 1.6× 26 663
Hiam Alquran Jordan 15 358 0.9× 122 0.4× 257 1.3× 131 0.9× 105 1.1× 71 816
Balázs Harangi Hungary 14 367 0.9× 320 1.0× 469 2.4× 142 0.9× 331 3.5× 51 948
Ebrahim Nasr-Esfahani Iran 7 263 0.7× 292 0.9× 141 0.7× 92 0.6× 154 1.6× 10 527
Jinman Kim Australia 7 270 0.7× 312 0.9× 109 0.6× 117 0.8× 124 1.3× 12 483
Isam Abu‐Qasmieh Jordan 13 226 0.6× 135 0.4× 178 0.9× 79 0.5× 77 0.8× 39 604

Countries citing papers authored by Ahmad Naeem

Since Specialization
Citations

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

Fields of papers citing papers by Ahmad Naeem

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ahmad Naeem

This figure shows the co-authorship network connecting the top 25 collaborators of Ahmad Naeem. A scholar is included among the top collaborators of Ahmad Naeem 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 Ahmad Naeem. Ahmad Naeem is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Naeem, Ahmad, et al.. (2025). SkinDWNet: a novel deep learning model for multiclass classification of skin cancers using dermoscopic images. Multimedia Systems. 31(4). 1 indexed citations
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Naeem, Ahmad, et al.. (2025). HYBRID APPROACH FOR INTRUSION DETECTION USING MACHINE LEARNING. 2(3). 33–45. 1 indexed citations
7.
Naeem, Ahmad & Tayyaba Anees. (2024). DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images. PLoS ONE. 19(3). e0297667–e0297667. 22 indexed citations
8.
Naeem, Ahmad, et al.. (2024). SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images. Mathematics. 12(7). 1030–1030. 35 indexed citations
9.
Naeem, Ahmad, et al.. (2023). Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study. Sensors. 23(20). 8457–8457. 24 indexed citations
10.
Malik, Hassaan, Ahmad Naeem, Rizwan Ali Naqvi, & Woong-Kee Loh. (2023). DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays. Sensors. 23(2). 743–743. 62 indexed citations
11.
Naeem, Ahmad, et al.. (2023). Deep Learning-Based Classification of Abrasion and Ischemic Diabetic Foot Sores Using Camera-Captured Images. Mathematics. 11(17). 3793–3793. 10 indexed citations
12.
Malik, Hassaan, Tayyaba Anees, Ahmad Naeem, Rizwan Ali Naqvi, & Woong-Kee Loh. (2023). Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans. Bioengineering. 10(2). 203–203. 23 indexed citations
13.
Naeem, Ahmad, et al.. (2023). DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers. 15(7). 2179–2179. 105 indexed citations breakdown →
14.
Malik, Hassaan, Ahmad Naeem, Shahzad Hassan, et al.. (2023). Multi-classification deep neural networks for identification of fish species using camera captured images. PLoS ONE. 18(4). e0284992–e0284992. 12 indexed citations
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Malik, Hassaan, Ahmad Naeem, Abolghasem Sadeghi‐Niaraki, Rizwan Ali Naqvi, & Seung-Won Lee. (2023). Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images. Complex & Intelligent Systems. 10(2). 2477–2497. 17 indexed citations
16.
Malik, Hassaan, et al.. (2023). Incorporating big data and IoT in intelligent ecosystems: state-of-the-arts, challenges and opportunities, and future directions. Multimedia Tools and Applications. 83(7). 20699–20741. 9 indexed citations
17.
Naeem, Ahmad, et al.. (2022). SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images. Sensors. 22(15). 5652–5652. 79 indexed citations
18.
Naeem, Ahmad, Tayyaba Anees, Rizwan Ali Naqvi, & Woong-Kee Loh. (2022). A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis. Journal of Personalized Medicine. 12(2). 275–275. 66 indexed citations
19.
Malik, Hassaan, et al.. (2022). CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays. Multimedia Tools and Applications. 82(9). 13855–13880. 73 indexed citations
20.
Naeem, Ahmad, Muhammad Shoaib Farooq, Adel Khelifi, & Adnan Abid. (2020). Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities. IEEE Access. 8. 110575–110597. 114 indexed citations

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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