Md Mamunur Rahaman

3.0k total citations · 4 hit papers
46 papers, 2.0k citations indexed

About

Md Mamunur Rahaman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Md Mamunur Rahaman has authored 46 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 17 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Md Mamunur Rahaman's work include AI in cancer detection (26 papers), Radiomics and Machine Learning in Medical Imaging (17 papers) and Cell Image Analysis Techniques (12 papers). Md Mamunur Rahaman is often cited by papers focused on AI in cancer detection (26 papers), Radiomics and Machine Learning in Medical Imaging (17 papers) and Cell Image Analysis Techniques (12 papers). Md Mamunur Rahaman collaborates with scholars based in China, United States and Germany. Md Mamunur Rahaman's co-authors include Chen Li, Yudong Yao, Marcin Grzegorzek, Hongzan Sun, Changhao Sun, Frank Kulwa, Shouliang Qi, Tao Jiang, Xiangchen Wu and Xiaoyan Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Md Mamunur Rahaman

41 papers receiving 2.0k 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.

DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques

2021 201 citations Md Mamunur Rahaman, Chen Li et al. Computers in Biology and Medicine profile →
GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection

2022 177 citations Haoyuan Chen, Chen Li et al. profile →
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

2022 159 citations Xintong Li, Chen Li et al. Artificial Intelligence Review profile →
CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron

2022 131 citations Wanli Liu, Chen Li et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Md Mamunur Rahaman China	21	1.3k	865	629	245	240	46	2.0k
Shan E Ahmed Raza United Kingdom	19	1.3k 1.0×	884 1.0×	727 1.2×	355 1.4×	52 0.2×	55	2.1k
Yuanpu Xie United States	13	901 0.7×	488 0.6×	598 1.0×	309 1.3×	66 0.3×	21	1.4k
Evgin Göçeri Türkiye	29	663 0.5×	628 0.7×	713 1.1×	73 0.3×	158 0.7×	52	1.8k
Ashnil Kumar Australia	21	875 0.7×	499 0.6×	712 1.1×	78 0.3×	205 0.9×	54	1.8k
Tao Jiang China	20	705 0.6×	347 0.4×	492 0.8×	204 0.8×	93 0.4×	113	1.6k
Shadi Albarqouni Germany	17	1.0k 0.8×	706 0.8×	577 0.9×	166 0.7×	64 0.3×	49	1.6k
Yutong Xie China	20	1.5k 1.2×	1.6k 1.9×	812 1.3×	82 0.3×	261 1.1×	62	3.2k
Mugahed A. Al–antari South Korea	24	1.6k 1.3×	1.1k 1.3×	508 0.8×	49 0.2×	167 0.7×	94	2.5k
Martin Urschler Austria	22	652 0.5×	789 0.9×	890 1.4×	127 0.5×	51 0.2×	87	2.4k
Mohammed A. Al‐masni South Korea	17	1.2k 0.9×	813 0.9×	443 0.7×	51 0.2×	298 1.2×	51	2.0k

Countries citing papers authored by Md Mamunur Rahaman

Since Specialization

Citations

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

Fields of papers citing papers by Md Mamunur Rahaman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Md Mamunur Rahaman. 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 Md Mamunur Rahaman. The network helps show where Md Mamunur Rahaman may publish in the future.

Co-authorship network of co-authors of Md Mamunur Rahaman

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Rahaman, Md Mamunur & Mohammad Islam Miah. (2025). Data-driven resilient model development and feature selection for rock compressive strength prediction using machine learning and transformer techniques. Earth Science Informatics. 18(3). 5 indexed citations

Rahaman, Md Mamunur, Ewan K.A. Millar, & Erik Meijering. (2025). Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images. IEEE Journal of Biomedical and Health Informatics. 30(1). 539–550.

Rahaman, Md Mamunur, et al.. (2025). Inertial subgradient splitting projection methods for solving equilibrium problems and applications. Numerical Algorithms.

Rahaman, Md Mamunur, et al.. (2025). Detection and prediction of slope stability in unsaturated finite slopes using interpretable machine learning. Modeling Earth Systems and Environment. 11(4). 3 indexed citations

Rahaman, Md Mamunur, Ewan K.A. Millar, & Erik Meijering. (2024). Histopathology Image Classification Using Supervised Contrastive Deep Learning. 1–5. 1 indexed citations

Rahaman, Md Mamunur, et al.. (2024). DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model. 139–142.

Li, Rui, Xiaoyan Li, Hongzan Sun, et al.. (2024). Few-shot learning based histopathological image classification of colorectal cancer. 4(4). 256–267. 8 indexed citations

Hu, Weiming, Chen Li, Md Mamunur Rahaman, et al.. (2023). EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation. Physica Medica. 107. 102534–102534. 22 indexed citations

Zhang, Jiawei, Sijuan Zou, Chen Li, et al.. (2023). TOD-Net: Transformer-Based Neural Network for Tiny Object Detection in Sperm Microscopic Videos. 1–5. 1 indexed citations

10.

Li, Chen, Md Mamunur Rahaman, Hao Xu, et al.. (2022). A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers. Frontiers in Microbiology. 13. 792166–792166. 39 indexed citations

11.

Zhang, Jiawei, Chen Li, Md Mamunur Rahaman, et al.. (2022). A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements. Archives of Computational Methods in Engineering. 30(1). 639–673. 14 indexed citations

12.

Hu, Weiming, Chen Li, Xiaoyan Li, et al.. (2022). GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer. Computers in Biology and Medicine. 142. 105207–105207. 67 indexed citations

13.

Chen, Haoyuan, Chen Li, Xiaoyan Li, et al.. (2022). IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach. Computers in Biology and Medicine. 143. 105265–105265. 101 indexed citations

14.

Li, Chen, Xintong Li, Md Mamunur Rahaman, et al.. (2021). A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches. arXiv (Cornell University). 7 indexed citations

15.

Li, Zihan, Chen Li, Yudong Yao, et al.. (2021). EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks. PLoS ONE. 16(5). e0250631–e0250631. 12 indexed citations

16.

Zhang, Jiawei, Chen Li, Md Mamunur Rahaman, et al.. (2021). A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artificial Intelligence Review. 55(4). 2875–2944. 101 indexed citations

17.

Li, Yixin, Chen Li, Changhao Sun, et al.. (2021). Intelligent Gastric Histopathology Image Classification Using Hierarchical Conditional Random Field based Attention Mechanism. 330–335. 6 indexed citations

18.

Li, Chen, Frank Kulwa, Md Mamunur Rahaman, et al.. (2020). Foldover Features for Dynamic Object Behaviour Description in Microscopic Videos. IEEE Access. 8. 114519–114540. 12 indexed citations

19.

Rahaman, Md Mamunur, Chen Li, Yudong Yao, et al.. (2020). Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. Journal of X-Ray Science and Technology. 28(5). 821–839. 212 indexed citations

20.

Xue, Dan, Zhou Xiao-min, Chen Li, et al.. (2020). An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification. IEEE Access. 8. 104603–104618. 121 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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