Mugahed A. Al–antari

4.0k total citations · 3 hit papers
94 papers, 2.5k citations indexed

About

Mugahed A. Al–antari is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mugahed A. Al–antari has authored 94 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Artificial Intelligence, 38 papers in Radiology, Nuclear Medicine and Imaging and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mugahed A. Al–antari's work include AI in cancer detection (29 papers), COVID-19 diagnosis using AI (22 papers) and Radiomics and Machine Learning in Medical Imaging (21 papers). Mugahed A. Al–antari is often cited by papers focused on AI in cancer detection (29 papers), COVID-19 diagnosis using AI (22 papers) and Radiomics and Machine Learning in Medical Imaging (21 papers). Mugahed A. Al–antari collaborates with scholars based in South Korea, China and Saudi Arabia. Mugahed A. Al–antari's co-authors include Seung‐Moo Han, Mohammed A. Al‐masni, Mun‐Taek Choi, Tae‐Seong Kim, Tae‐Seong Kim, Tae‐Yeon Kim, Patricio Rivera, Edwin Valarezo Añazco, Abdullah Y. Muaad and Zaid Al‐Huda and has published in prestigious journals such as Scientific Reports, IEEE Access and Sensors.

In The Last Decade

Mugahed A. Al–antari

77 papers receiving 2.4k 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.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

2018 345 citations Mohammed A. Al‐masni, Mugahed A. Al–antari et al. Computer Methods and Programs in Biomedicine profile →
Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

2018 338 citations Mohammed A. Al‐masni, Mugahed A. Al–antari et al. Computer Methods and Programs in Biomedicine profile →
A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification

2018 292 citations Mugahed A. Al–antari, Mohammed A. Al‐masni et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mugahed A. Al–antari South Korea	24	1.6k	1.1k	508	368	306	94	2.5k
J. Shin United States	7	1.1k 0.7×	1.0k 0.9×	848 1.7×	216 0.6×	246 0.8×	7	2.5k
Md Mamunur Rahaman China	21	1.3k 0.8×	865 0.8×	629 1.2×	220 0.6×	164 0.5×	46	2.0k
Burhan Ergen Türkiye	25	1.2k 0.7×	1.1k 1.0×	878 1.7×	97 0.3×	477 1.6×	90	2.6k
Mohammed A. Al‐masni South Korea	17	1.2k 0.7×	813 0.7×	443 0.9×	556 1.5×	292 1.0×	51	2.0k
Zafer Cömert Türkiye	31	1.5k 1.0×	1.4k 1.2×	858 1.7×	119 0.3×	540 1.8×	78	3.5k
Yutong Xie China	20	1.5k 1.0×	1.6k 1.4×	812 1.6×	622 1.7×	237 0.8×	62	3.2k
Mesut Toğaçar Türkiye	22	1.2k 0.7×	1.2k 1.0×	761 1.5×	106 0.3×	489 1.6×	57	2.3k
İshak Paçal Türkiye	25	699 0.4×	600 0.5×	373 0.7×	353 1.0×	257 0.8×	63	1.8k
Majed Alhaisoni Saudi Arabia	29	1.0k 0.6×	671 0.6×	875 1.7×	335 0.9×	574 1.9×	104	2.5k
Jacinto C. Nascimento Portugal	26	1.1k 0.7×	802 0.7×	1.4k 2.9×	373 1.0×	136 0.4×	113	3.1k

Countries citing papers authored by Mugahed A. Al–antari

Since Specialization

Citations

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

Fields of papers citing papers by Mugahed A. Al–antari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mugahed A. Al–antari. 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 Mugahed A. Al–antari. The network helps show where Mugahed A. Al–antari may publish in the future.

Co-authorship network of co-authors of Mugahed A. Al–antari

This figure shows the co-authorship network connecting the top 25 collaborators of Mugahed A. Al–antari. A scholar is included among the top collaborators of Mugahed A. Al–antari 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 Mugahed A. Al–antari. Mugahed A. Al–antari 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

Kwon, Hyunwook, et al.. (2025). Multimodal Knowledge-Infused VLM for Respiratory Disease Prediction and Clinical Report Generation. IEEE Journal of Biomedical and Health Informatics. PP. 1–14.

Zhang, Xiaoling, Happy Nkanta Monday, Grace Ugochi Nneji, et al.. (2025). Advancements, Challenges, and Future Directions in Scene-Graph-Based Image Generation: A Comprehensive Review. Electronics. 14(6). 1158–1158.

Zhi-guang, Qin, et al.. (2025). A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities. Scientific Reports. 15(1). 29810–29810.

Samee, Nagwan Abdel, Mohammed A. Al‐masni, Mugahed A. Al–antari, et al.. (2025). New Gait Representation Maps for Enhanced Recognition in Clinical Gait Analysis. Bioengineering. 12(10). 1130–1130.

Al–antari, Mugahed A., et al.. (2025). Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review. Artificial Intelligence Review. 58(8).

Saleh, Radhwan A. A., et al.. (2025). Enhancing IoT Security Through the Integration of Explainable AI and Ensemble Xgboost for Improved Intrusion Detection. Kocaeli Üniversitesi - AVESİS. 1–7.

Alshamrani, Sultan S., et al.. (2024). Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images. Journal of Applied Biomedicine. 44(3). 731–758. 21 indexed citations

Saleh, Radhwan A. A., et al.. (2024). A hybrid deep learning skin cancer prediction framework. Engineering Science and Technology an International Journal. 57. 101818–101818. 20 indexed citations

Qin, Zhiguang, et al.. (2024). CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images. Bioengineering. 11(9). 887–887. 3 indexed citations

10.

Al‐masni, Mohammed A., Mugahed A. Al–antari, Maali Alabdulhafith, et al.. (2024). Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning. Bioengineering. 11(5). 477–477. 1 indexed citations

11.

Yasmin, Mussarat, et al.. (2024). A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification. Multimedia Tools and Applications. 83(39). 86923–86954. 1 indexed citations

12.

Biros, M., et al.. (2024). Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study. Diagnostics. 14(11). 1117–1117. 1 indexed citations

13.

Chen, Wenyu, et al.. (2024). DecoStrat: Leveraging the Capabilities of Language Models in D2T Generation via Decoding Framework. Mathematics. 12(22). 3596–3596.

14.

Al‐Huda, Zaid, et al.. (2023). A hybrid deep learning pavement crack semantic segmentation. Engineering Applications of Artificial Intelligence. 122. 106142–106142. 81 indexed citations

15.

Al–antari, Mugahed A., et al.. (2023). XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans. Kocaeli Üniversitesi - AVESİS. 28. 1–5.

16.

Ukwuoma, Chiagoziem C., Dongsheng Cai, Md Belal Bin Heyat, et al.. (2023). Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images. Journal of King Saud University - Computer and Information Sciences. 35(7). 101596–101596. 35 indexed citations

17.

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

18.

Samee, Nagwan Abdel, et al.. (2022). A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms. Biomedicines. 10(11). 2971–2971. 45 indexed citations

19.

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

20.

Al‐masni, Mohammed A., Mugahed A. Al–antari, Jeongmin Park, et al.. (2018). Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Computer Methods and Programs in Biomedicine. 157. 85–94. 345 indexed citations breakdown →

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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