Dina A. Ragab

958 total citations · 1 hit paper
9 papers, 648 citations indexed

About

Dina A. Ragab is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dina A. Ragab has authored 9 papers receiving a total of 648 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dina A. Ragab's work include Radiomics and Machine Learning in Medical Imaging (5 papers), AI in cancer detection (5 papers) and COVID-19 diagnosis using AI (5 papers). Dina A. Ragab is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), AI in cancer detection (5 papers) and COVID-19 diagnosis using AI (5 papers). Dina A. Ragab collaborates with scholars based in Egypt and United Kingdom. Dina A. Ragab's co-authors include Maha Sharkas, Jinchang Ren, Stephen Marshall, Omneya Attallah and Noha Ghatwary and has published in prestigious journals such as Computers in Biology and Medicine, PeerJ and Biomedical Signal Processing and Control.

In The Last Decade

Dina A. Ragab

9 papers receiving 612 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.

Breast cancer detection using deep convolutional neural networks and support vector machines

2019 320 citations Dina A. Ragab, Maha Sharkas et al. PeerJ profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dina A. Ragab Egypt	8	502	423	140	135	49	9	648
Neeraj Dhungel Australia	8	517 1.0×	421 1.0×	106 0.8×	184 1.4×	81 1.7×	10	621
Andrik Rampun United Kingdom	12	504 1.0×	401 0.9×	82 0.6×	200 1.5×	120 2.4×	21	629
Nisreen I. R. Yassin Egypt	6	278 0.6×	229 0.5×	68 0.5×	155 1.1×	26 0.5×	12	431
Yangqin Feng Singapore	12	232 0.5×	258 0.6×	47 0.3×	165 1.2×	22 0.4×	20	444
Shahzad Akbar Pakistan	16	227 0.5×	474 1.1×	205 1.5×	318 2.4×	26 0.5×	49	772
Zizhou Wang China	10	232 0.5×	171 0.4×	56 0.4×	117 0.9×	20 0.4×	27	344
Tianyu Shi China	9	153 0.3×	207 0.5×	89 0.6×	168 1.2×	37 0.8×	16	405
Shaimaa Omran Egypt	8	230 0.5×	188 0.4×	51 0.4×	54 0.4×	24 0.5×	13	384
Nor Aniza Azmi Malaysia	5	199 0.4×	198 0.5×	52 0.4×	69 0.5×	37 0.8×	15	309
Kiran Jabeen Pakistan	8	254 0.5×	188 0.4×	91 0.7×	80 0.6×	13 0.3×	8	330

Countries citing papers authored by Dina A. Ragab

Since Specialization

Citations

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

Fields of papers citing papers by Dina A. Ragab

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dina A. Ragab. 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 Dina A. Ragab. The network helps show where Dina A. Ragab may publish in the future.

Co-authorship network of co-authors of Dina A. Ragab

This figure shows the co-authorship network connecting the top 25 collaborators of Dina A. Ragab. A scholar is included among the top collaborators of Dina A. Ragab 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 Dina A. Ragab. Dina A. Ragab 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:

9 of 9 papers shown

1.

Ragab, Dina A., et al.. (2024). DeepCSFusion: Deep Compressive Sensing Fusion for Efficient COVID-19 Classification. Journal of Imaging Informatics in Medicine. 37(4). 1346–1358. 1 indexed citations

2.

Ragab, Dina A., et al.. (2023). Urban spaces as a positive catalyst during pandemics: Assessing the community’s well-being by using artificial intelligence techniques. Ain Shams Engineering Journal. 14(5). 102084–102084. 7 indexed citations

3.

Attallah, Omneya & Dina A. Ragab. (2022). Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomedical Signal Processing and Control. 80. 104273–104273. 28 indexed citations

4.

Ragab, Dina A., Omneya Attallah, Maha Sharkas, Jinchang Ren, & Stephen Marshall. (2021). A framework for breast cancer classification using Multi-DCNNs. Computers in Biology and Medicine. 131. 104245–104245. 137 indexed citations

5.

Ragab, Dina A. & Omneya Attallah. (2020). FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features. PeerJ Computer Science. 6. e306–e306. 47 indexed citations

6.

Attallah, Omneya, Dina A. Ragab, & Maha Sharkas. (2020). MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks. PeerJ. 8. e10086–e10086. 46 indexed citations

7.

Ragab, Dina A., Maha Sharkas, Stephen Marshall, & Jinchang Ren. (2019). Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ. 7. e6201–e6201. 320 indexed citations breakdown →

8.

Ragab, Dina A., Maha Sharkas, & Omneya Attallah. (2019). Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers. Diagnostics. 9(4). 165–165. 55 indexed citations

9.

Sharkas, Maha, et al.. (2011). Detection of Microcalcifications in Mammograms Using Support Vector Machine. 1. 179–184. 7 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.

Explore authors with similar magnitude of impact