Arshia Rehman

1.1k total citations · 1 hit paper
9 papers, 673 citations indexed

About

Arshia Rehman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Arshia Rehman has authored 9 papers receiving a total of 673 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 3 papers in Media Technology. Recurrent topics in Arshia Rehman's work include Handwritten Text Recognition Techniques (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Image Processing and 3D Reconstruction (2 papers). Arshia Rehman is often cited by papers focused on Handwritten Text Recognition Techniques (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Image Processing and 3D Reconstruction (2 papers). Arshia Rehman collaborates with scholars based in Pakistan, Australia and Saudi Arabia. Arshia Rehman's co-authors include Saeeda Naz, Imran Razzak, Faiza Akram, Muhammad Imran, Ibrahim A. Hameed, Fiaz Gul Khan, Sharifullah Khan, Gwo‐Jen Hwang, Muhammad Azeem Abbas and Ahmad Khan and has published in prestigious journals such as IEEE Access, Artificial Intelligence Review and British Journal of Educational Technology.

In The Last Decade

Arshia Rehman

9 papers receiving 635 citations

Hit Papers

align trajectories

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.

A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

2019 432 citations Arshia Rehman, Saeeda Naz et al. Circuits Systems and Signal Processing profile →

Peers

Arshia Rehman

CVPR

NEURO

AI

RNMI

MT

Asim Munir Pakistan

Shafqat Ali Shad United States

T. Kalaiselvi India

Jyostna Devi Bodapati India

N. Veeranjaneyulu India

Abdel-Badeeh M. Salem Egypt

Waheed Ahmed Abro China

Jing Xue China

Arfan Jaffar Pakistan

Subhashis Banerjee India

Asim Munir Pakistan

Arshia Rehman

245 ×0.5

CVPR

167 ×0.4

NEURO

186 ×0.7

AI

134 ×0.8

RNMI

43 ×0.8

MT

Citations per year, relative to Arshia Rehman Arshia Rehman (= 1×) peers Asim Munir

Countries citing papers authored by Arshia Rehman

Since Specialization

Citations

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

Fields of papers citing papers by Arshia Rehman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arshia Rehman

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

All Works

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9 of 9 papers shown

1.

Shehzadi, Naureen, et al.. (2025). Machine learning and deep learning based psoriasis recognition system: evaluation, management, prognosis—where we are and the way to the future. Artificial Intelligence Review. 58(9). 1 indexed citations

2.

Rehman, Arshia, et al.. (2023). Review on chest pathogies detection systems using deep learning techniques. Artificial Intelligence Review. 56(11). 12607–12653. 10 indexed citations

3.

Rehman, Arshia & Fiaz Gul Khan. (2020). A deep learning based review on abdominal images. Multimedia Tools and Applications. 80(20). 30321–30352. 17 indexed citations

4.

Rehman, Arshia, Saeeda Naz, Usman Naseem, Imran Razzak, & Ibrahim A. Hameed. (2019). Deep AutoEncoder-Decoder Framework for Semantic Segmentation of Brain Tumor.. 15. 53–60. 11 indexed citations

5.

Naz, Saeeda, et al.. (2019). Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities. Multimedia Tools and Applications. 79(1-2). 289–340. 43 indexed citations

6.

Rehman, Arshia, Saeeda Naz, Imran Razzak, Faiza Akram, & Muhammad Imran. (2019). A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits Systems and Signal Processing. 39(2). 757–775. 432 indexed citations breakdown →

7.

Rehman, Arshia, Saeeda Naz, Imran Razzak, & Ibrahim A. Hameed. (2019). Automatic Visual Features for Writer Identification: A Deep Learning Approach. IEEE Access. 7. 17149–17157. 71 indexed citations

8.

Rehman, Arshia, Saeeda Naz, & Imran Razzak. (2018). Writer identification using machine learning approaches: a comprehensive review. Multimedia Tools and Applications. 78(8). 10889–10931. 65 indexed citations

9.

Khan, Sharifullah, Gwo‐Jen Hwang, Muhammad Azeem Abbas, & Arshia Rehman. (2018). Mitigating the urban–rural educational gap in developing countries through mobile technology‐supported learning. British Journal of Educational Technology. 50(2). 735–749. 23 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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