Maha Sharkas

1.7k total citations · 1 hit paper
40 papers, 1.1k citations indexed

About

Maha Sharkas is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Maha Sharkas has authored 40 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 11 papers in Signal Processing. Recurrent topics in Maha Sharkas's work include AI in cancer detection (8 papers), Image Retrieval and Classification Techniques (7 papers) and Advanced Steganography and Watermarking Techniques (6 papers). Maha Sharkas is often cited by papers focused on AI in cancer detection (8 papers), Image Retrieval and Classification Techniques (7 papers) and Advanced Steganography and Watermarking Techniques (6 papers). Maha Sharkas collaborates with scholars based in Egypt, United Kingdom and Canada. Maha Sharkas's co-authors include Omneya Attallah, Dina A. Ragab, Stephen Marshall, Jinchang Ren, Mohamed Tamazin, Mohamed Khedr, Ehab F. Badran, Ibrahim El Rube, Onsy Abdel Alim and Mohamed Mahmoud and has published in prestigious journals such as Scientific Reports, Sensors and Computers in Biology and Medicine.

In The Last Decade

Maha Sharkas

39 papers receiving 1.1k citations

Hit Papers

Breast cancer detection using deep convolutional neural n... 2019 2026 2021 2023 2019 100 200 300
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. 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 Career Trend Papers Cites
Maha Sharkas Egypt 17 636 523 318 168 132 40 1.1k
Fatma Taher United Arab Emirates 21 464 0.7× 483 0.9× 297 0.9× 130 0.8× 101 0.8× 110 1.3k
Shivajirao M. Jadhav India 15 474 0.7× 388 0.7× 240 0.8× 120 0.7× 37 0.3× 25 1.2k
S. Ravi India 15 498 0.8× 170 0.3× 330 1.0× 84 0.5× 171 1.3× 59 1.2k
J. Dinesh Peter India 18 319 0.5× 316 0.6× 272 0.9× 76 0.5× 34 0.3× 66 821
Min Xian United States 18 815 1.3× 500 1.0× 622 2.0× 166 1.0× 26 0.2× 59 1.3k
Fatih Özyurt Türkiye 15 723 1.1× 640 1.2× 779 2.4× 398 2.4× 70 0.5× 63 1.6k
N. Sri Madhava Raja India 16 464 0.7× 543 1.0× 488 1.5× 239 1.4× 13 0.1× 46 1.3k
Mustafa Ghaderzadeh Iran 17 343 0.5× 296 0.6× 236 0.7× 27 0.2× 34 0.3× 28 810
Sukadev Meher India 18 629 1.0× 429 0.8× 872 2.7× 144 0.9× 55 0.4× 104 1.3k

Countries citing papers authored by Maha Sharkas

Since Specialization
Citations

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

Fields of papers citing papers by Maha Sharkas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maha Sharkas

This figure shows the co-authorship network connecting the top 25 collaborators of Maha Sharkas. A scholar is included among the top collaborators of Maha Sharkas 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 Maha Sharkas. Maha Sharkas 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.
2.
Sharkas, Maha & Omneya Attallah. (2024). Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform. Scientific Reports. 14(1). 6914–6914. 25 indexed citations
3.
Attallah, Omneya & Maha Sharkas. (2021). GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases. PeerJ Computer Science. 7. e423–e423. 39 indexed citations
4.
Attallah, Omneya & Maha Sharkas. (2021). Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories. Contrast Media & Molecular Imaging. 2021. 1–14. 20 indexed citations
5.
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
6.
Sharkas, Maha, et al.. (2020). A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans. Diagnostics. 10(3). 131–131. 35 indexed citations
7.
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
8.
Sharkas, Maha, et al.. (2020). A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans. PeerJ Computer Science. 6. e303–e303. 31 indexed citations
9.
Attallah, Omneya, et al.. (2020). Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders. Diagnostics. 10(1). 27–27. 46 indexed citations
10.
Tamazin, Mohamed, et al.. (2019). Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization. Sensors. 19(7). 1678–1678. 10 indexed citations
11.
Attallah, Omneya, et al.. (2019). Automatic Classification of Gastrointestinal Diseases Based on Machine Learning Techniques. 8. 85–89. 2 indexed citations
12.
Sharkas, Maha, et al.. (2017). Priority level mutualism for emergency vehicle using game theory. 75–80. 2 indexed citations
13.
Tamazin, Mohamed, et al.. (2016). An enhanced WiFi indoor localization system based on machine learning. 1–8. 111 indexed citations
14.
Sharkas, Maha. (2016). A neural network based approach for iris recognition based on both eyes. 253–258. 10 indexed citations
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Badran, Ehab F., Maha Sharkas, & Omneya Attallah. (2009). Multiple watermark embedding scheme in wavelet-spatial domains based on ROI of medical images. 1–8. 17 indexed citations
18.
Sharkas, Maha, et al.. (2007). A Novel Dual-Purpose Image Watermarking Technique. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations
19.
Sharkas, Maha, et al.. (2006). Identification of transmission line faults using wavelet analysis. 3. 1108–1111. 5 indexed citations
20.
Sharkas, Maha. (2005). Application of DCT blocks with principal component analysis for face recognition. 107–111. 10 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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