Omneya Attallah

2.7k total citations · 2 hit papers
72 papers, 1.9k citations indexed

About

Omneya Attallah is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Omneya Attallah has authored 72 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 29 papers in Radiology, Nuclear Medicine and Imaging and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Omneya Attallah's work include AI in cancer detection (26 papers), COVID-19 diagnosis using AI (16 papers) and Radiomics and Machine Learning in Medical Imaging (16 papers). Omneya Attallah is often cited by papers focused on AI in cancer detection (26 papers), COVID-19 diagnosis using AI (16 papers) and Radiomics and Machine Learning in Medical Imaging (16 papers). Omneya Attallah collaborates with scholars based in Egypt, United Kingdom and Türkiye. Omneya Attallah's co-authors include Maha Sharkas, Dina A. Ragab, Rania A. Ibrahim, Nahla E. Zakzouk, Stephen Marshall, Jinchang Ren, Xianghong Ma, Iman Morsi, İshak Paçal and Kadir Sabancı and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Omneya Attallah

70 papers receiving 1.8k citations

Hit Papers

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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.

InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

2025 23 citations İshak Paçal, Omneya Attallah Biomedical Signal Processing and Control profile →
Hybrid deep learning model for automated colorectal cancer detection using local and global feature extraction

2025 18 citations İshak Paçal, Omneya Attallah Knowledge-Based Systems profile →

Peers

Countries citing papers authored by Omneya Attallah

Since Specialization

Citations

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

Fields of papers citing papers by Omneya Attallah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Omneya Attallah

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Comparative evaluation of lightweight convolutional neural network and vision transformer models for multi-class brain tumor classification using merged large MRI datasets 2025 ·Chemometrics and Intelligent Laboratory Systems ·Omneya Attallah, İshak Paçal	0
2	Hybrid deep learning model for automated colorectal cancer detection using local and global feature extraction breakdown → 2025 ·Knowledge-Based Systems ·İshak Paçal, Omneya Attallah	18
3	Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection 2025 ·Scientific Reports ·Omneya Attallah	2
4	A lightweight deep learning framework for transformer fault diagnosis in smart grids using multiple scale CNN features 2025 ·Scientific Reports ·Omneya Attallah, Rania A. Ibrahim, Nahla E. Zakzouk	5
5	InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis breakdown → 2025 ·Biomedical Signal Processing and Control ·İshak Paçal, Omneya Attallah	23
6	Impact of magnification on deep learning approaches through comprehensive comparative study of histopathological breast cancer classification 2025 ·Biomedical Signal Processing and Control ·Omneya Attallah, İshak Paçal	2
7	Acute lymphocytic leukemia detection and subtype classification via extended wavelet pooling based-CNNs and statistical-texture features 2024 ·Image and Vision Computing ·Omneya Attallah	12
8	Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets 2024 ·Scientific Reports ·Omneya Attallah	12
9	Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities 2024 ·Measurement ·(unknown), Ahmed Onsy, Omneya Attallah, Hadley Brooks, Iman Morsi	1
10	ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection 2024 ·Biomimetics ·Omneya Attallah	14
11	Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning 2024 ·Computers in Biology and Medicine ·Omneya Attallah	34
12	Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform 2024 ·Scientific Reports ·Maha Sharkas, Omneya Attallah	25
13	DEEP LEARNING-BASED MODEL FOR PADDY DISEASES CLASSIFICATION BY THERMAL INFRARED SENSOR: AN APPLICATION FOR PRECISION AGRICULTURE 2023 ·SHILAP Revista de lepidopterología ·Omneya Attallah	2
14	Multitask Deep Learning-Based Pipeline for Gas Leakage Detection via E-Nose and Thermal Imaging Multimodal Fusion 2023 ·Chemosensors ·Omneya Attallah	28
15	Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection 2023 ·Horticulturae ·Omneya Attallah	65
16	GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks 2023 ·Diagnostics ·Omneya Attallah	28
17	A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices 2022 ·Applied Soft Computing ·Omneya Attallah, Ahmed Samir	35
18	FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features 2020 ·PeerJ Computer Science ·Dina A. Ragab, Omneya Attallah	47
19	Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention 2017 ·BMC Medical Informatics and Decision Making ·Omneya Attallah, Alan Karthikesalingam, Peter Holt, Matthew M. Thompson, (unknown), Matthew J. Bown, (unknown), Xianghong Ma	35
20	Multiple watermark embedding scheme in wavelet-spatial domains based on ROI of medical images 2009 ·Ehab F. Badran, Maha Sharkas, Omneya Attallah	17

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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