Ghada Atteia

1.0k total citations
45 papers, 657 citations indexed

About

Ghada Atteia is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ghada Atteia has authored 45 papers receiving a total of 657 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 9 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ghada Atteia's work include AI in cancer detection (7 papers), Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers) and COVID-19 diagnosis using AI (6 papers). Ghada Atteia is often cited by papers focused on AI in cancer detection (7 papers), Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers) and COVID-19 diagnosis using AI (6 papers). Ghada Atteia collaborates with scholars based in Saudi Arabia, South Korea and Egypt. Ghada Atteia's co-authors include Nagwan Abdel Samee, Michael Collins, Michael Denbina, Amel Ali Alhussan, El‐Sayed M. El‐kenawy, Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Atif Rizwan, Mona Jamjoom, Yasser M. Kadah and Mugahed A. Al–antari and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Ghada Atteia

37 papers receiving 634 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ghada Atteia Saudi Arabia 16 212 162 157 127 94 45 657
Badri Narayan Subudhi India 18 159 0.8× 563 3.5× 50 0.3× 89 0.7× 27 0.3× 78 864
Muhammad Jaleed Khan Pakistan 14 259 1.2× 419 2.6× 130 0.8× 63 0.5× 42 0.4× 36 1.2k
Li Wei China 9 181 0.9× 415 2.6× 47 0.3× 85 0.7× 17 0.2× 55 1.8k
Xiaokang Chen China 11 408 1.9× 846 5.2× 184 1.2× 92 0.7× 77 0.8× 38 1.2k
Xia Xu China 23 204 1.0× 555 3.4× 34 0.2× 107 0.8× 11 0.1× 58 1.5k
Deepak Adhikari China 14 134 0.6× 73 0.5× 22 0.1× 85 0.7× 31 0.3× 33 564
Hongbing Ma China 19 110 0.5× 525 3.2× 41 0.3× 124 1.0× 18 0.2× 93 1.0k
Tianming Zhan China 17 144 0.7× 381 2.4× 73 0.5× 40 0.3× 89 0.9× 60 898
T. Veerakumar India 15 98 0.5× 755 4.7× 50 0.3× 41 0.3× 25 0.3× 63 952
Antonio Pertusa Spain 14 249 1.2× 363 2.2× 282 1.8× 90 0.7× 7 0.1× 32 892

Countries citing papers authored by Ghada Atteia

Since Specialization
Citations

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

Fields of papers citing papers by Ghada Atteia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ghada Atteia

This figure shows the co-authorship network connecting the top 25 collaborators of Ghada Atteia. A scholar is included among the top collaborators of Ghada Atteia 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 Ghada Atteia. Ghada Atteia 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
1.
Atteia, Ghada, et al.. (2025). Deep learning-based decision support system for cervical cancer identification in liquid-based cytology pap smears. Technology and Health Care. 33(5). 2194–2210.
2.
Emara, Abdel-Hamid M., Ghada Atteia, & Jawad Hasan Alkhateeb. (2025). Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm. IEEE Access. 13. 134506–134518.
3.
Ali, Abid, et al.. (2024). Brain Tumor Segmentation Using Generative Adversarial Networks. IEEE Access. 12. 183525–183541. 2 indexed citations
4.
Rizwan, Atif, et al.. (2024). Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach. SHILAP Revista de lepidopterología. 79. 85–85. 2 indexed citations
5.
Alkanhel, Reem, Ghada Atteia, Mohamed Amine Ouamri, et al.. (2024). Next-Gen Connectivity: Exploring UAV Swarm Applications in Cell-Free Massive MIMO for IIoT. IEEE Access. 12. 152045–152057.
6.
Malik, Fazila‐Tun‐Nesa, et al.. (2024). A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection. Mathematics. 12(12). 1799–1799. 5 indexed citations
7.
Dabboor, Mohammed, et al.. (2023). Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data. Remote Sensing. 15(7). 1916–1916. 17 indexed citations
8.
Atteia, Ghada, et al.. (2023). Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification. Diagnostics. 13(16). 2672–2672. 5 indexed citations
9.
10.
Dabboor, Mohammed, et al.. (2023). Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques. Land. 12(10). 1861–1861. 3 indexed citations
11.
Rizwan, Atif, et al.. (2023). Arabic Sentiment Analysis and Sarcasm Detection Using Probabilistic Projections-Based Variational Switch Transformer. IEEE Access. 11. 67865–67881. 8 indexed citations
12.
13.
Samee, Nagwan Abdel, Ghada Atteia, Souham Meshoul, Mugahed A. Al–antari, & Yasser M. Kadah. (2022). Deep Learning Cascaded Feature Selection Framework for Breast Cancer Classification: Hybrid CNN with Univariate-Based Approach. Mathematics. 10(19). 3631–3631. 35 indexed citations
14.
Samee, Nagwan Abdel, Amel Ali Alhussan, Ghada Atteia, et al.. (2022). A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms. Sensors. 22(13). 4938–4938. 41 indexed citations
15.
Atteia, Ghada, Michael Collins, Abeer D. Algarni, & Nagwan Abdel Samee. (2022). Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data. Remote Sensing. 14(21). 5569–5569. 5 indexed citations
16.
Atteia, Ghada, Amel Ali Alhussan, & Nagwan Abdel Samee. (2022). BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images. Sensors. 22(15). 5520–5520. 40 indexed citations
17.
Samee, Nagwan Abdel, et al.. (2022). Hybrid Feature Reduction Using PCC-Stacked Autoencoders for Gold/Oil Prices Forecasting under COVID-19 Pandemic. Electronics. 11(7). 991–991. 11 indexed citations
18.
Rizwan, Atif, et al.. (2022). Aggression Detection in Social Media from Textual Data Using Deep Learning Models. Applied Sciences. 12(10). 5083–5083. 32 indexed citations
19.
Samee, Nagwan Abdel, Noha F. Mahmoud, Ghada Atteia, et al.. (2022). Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics. 12(10). 2541–2541. 35 indexed citations
20.
Atteia, Ghada, et al.. (2021). DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis. Entropy. 23(10). 1251–1251. 20 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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