Nawrès Khlifa

854 total citations
60 papers, 524 citations indexed

About

Nawrès Khlifa is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Neurology. According to data from OpenAlex, Nawrès Khlifa has authored 60 papers receiving a total of 524 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Radiology, Nuclear Medicine and Imaging, 23 papers in Computer Vision and Pattern Recognition and 8 papers in Neurology. Recurrent topics in Nawrès Khlifa's work include Radiomics and Machine Learning in Medical Imaging (12 papers), Image and Signal Denoising Methods (12 papers) and Retinal Imaging and Analysis (11 papers). Nawrès Khlifa is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (12 papers), Image and Signal Denoising Methods (12 papers) and Retinal Imaging and Analysis (11 papers). Nawrès Khlifa collaborates with scholars based in Tunisia, France and Algeria. Nawrès Khlifa's co-authors include Rostom Mabrouk, Kamel Hamrouni, Noureddine Ellouze, Djamel Benmerzoug, Hedi Tabia, François Brémond, Haithem Hermessi, Belkacem Chikhaoui, Désiré Sidibé and Aladine Chetouani and has published in prestigious journals such as IEEE Access, Applied Soft Computing and Biomedical Signal Processing and Control.

In The Last Decade

Nawrès Khlifa

54 papers receiving 495 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nawrès Khlifa Tunisia 12 262 120 112 110 76 60 524
Zhuangzhi Yan China 16 301 1.1× 135 1.1× 133 1.2× 61 0.6× 38 0.5× 87 749
Paweł Badura Poland 12 107 0.4× 104 0.9× 94 0.8× 42 0.4× 43 0.6× 34 349
Yin Dai China 9 156 0.6× 162 1.4× 214 1.9× 25 0.2× 23 0.3× 14 455
Tassilo Klein Germany 12 214 0.8× 160 1.3× 269 2.4× 35 0.3× 55 0.7× 28 632
Nina Zhou United States 11 120 0.5× 121 1.0× 70 0.6× 26 0.2× 27 0.4× 34 533
Haseeb Hassan China 13 166 0.6× 93 0.8× 151 1.3× 108 1.0× 12 0.2× 45 514
Hung N. Pham Vietnam 8 120 0.5× 145 1.2× 57 0.5× 34 0.3× 10 0.1× 19 365
Mohammad Faizal Ahmad Fauzi Malaysia 15 174 0.7× 206 1.7× 334 3.0× 33 0.3× 21 0.3× 111 667
Muhammad Nadeem Pakistan 11 50 0.2× 75 0.6× 203 1.8× 40 0.4× 17 0.2× 63 429
Chenchu Xu China 15 368 1.4× 105 0.9× 245 2.2× 51 0.5× 26 0.3× 35 741

Countries citing papers authored by Nawrès Khlifa

Since Specialization
Citations

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

Fields of papers citing papers by Nawrès Khlifa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nawrès Khlifa

This figure shows the co-authorship network connecting the top 25 collaborators of Nawrès Khlifa. A scholar is included among the top collaborators of Nawrès Khlifa 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 Nawrès Khlifa. Nawrès Khlifa 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.
Tabia, Hedi, et al.. (2025). A Deep Learning Approach for Predicting the Response to Anti-VEGF Treatment in Diabetic Macular Edema Patients Using Optical Coherence Tomography Images. SPIRE - Sciences Po Institutional REpository. 453–462. 1 indexed citations
2.
Khlifa, Nawrès, et al.. (2025). Integrating Deep Learning and SHAP for Breast Cancer Classification and Biomarker Discovery Using Gene Expression Data. IEEE Access. 13. 49693–49709. 3 indexed citations
3.
4.
Sidibé, Désiré, et al.. (2025). Artificial Intelligence Cad Systems for the Detection and Classification of Retinal Diseases from Oct Images: A Review. SSRN Electronic Journal. 1 indexed citations
5.
Khlifa, Nawrès, et al.. (2025). An Appearance-based VisionTransformer Network for Enhanced Gaze Estimation. Signal Image and Video Processing. 19(9).
6.
Hermessi, Haithem, et al.. (2024). Optimizing gene selection for Alzheimer’s disease classification: A Bayesian approach to filter and embedded techniques. Applied Soft Computing. 167. 112307–112307. 1 indexed citations
7.
Sidibé, Désiré, et al.. (2024). A new intelligent system based deep learning to detect DME and AMD in OCT images. International Ophthalmology. 44(1). 191–191. 6 indexed citations
8.
Khlifa, Nawrès, et al.. (2023). Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks. Scientific African. 20. e01679–e01679. 10 indexed citations
9.
Khlifa, Nawrès, et al.. (2023). Bilinear Pooling for Thyroid Nodule Classification in Ultrasound Imaging. Arabian Journal for Science and Engineering. 48(8). 10563–10573. 8 indexed citations
10.
Sidibé, Désiré, et al.. (2023). Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning. International Journal of Biomedical Imaging. 2023. 1–10. 4 indexed citations
11.
Khlifa, Nawrès, et al.. (2022). Anatomical prognosis after idiopathic macular hole surgery: machine learning based-predection. Libyan Journal of Medicine. 17(1). 2034334–2034334. 8 indexed citations
12.
Mabrouk, Rostom, et al.. (2020). Validation of iterative multi-resolution method for partial volume correction and quantification improvement in PET image. Biomedical Signal Processing and Control. 60. 101954–101954. 2 indexed citations
13.
Khlifa, Nawrès, et al.. (2019). Automatic detection of intracranial aneurysm using LBP and Fourier descriptor in angiographic images. International Journal of Computer Assisted Radiology and Surgery. 14(8). 1353–1364. 7 indexed citations
14.
Khlifa, Nawrès, et al.. (2018). A morphological operation-based approach for Sub-pleural lung nodule detection from CT images. 84–89. 7 indexed citations
15.
Khlifa, Nawrès, et al.. (2018). A review on Deep Learning in thyroid ultrasound Computer-Assisted Diagnosis systems. 291–297. 15 indexed citations
16.
Khlifa, Nawrès, et al.. (2018). Automated Computerized Method for the Detection of Unruptured Cerebral Aneurysms in DSA Images. Current Medical Imaging Formerly Current Medical Imaging Reviews. 14(5). 771–777. 6 indexed citations
17.
Barhoumi, Walid, et al.. (2015). Integration of a Fuzzy Spatial Constraint into Active Shape Models for ROI Detection in Medical Images. Current Medical Imaging Formerly Current Medical Imaging Reviews. 11(1). 15–22. 1 indexed citations
18.
Noblet, Vincent, et al.. (2012). BINARY IMAGE REGISTRATION BASED ON GEOMETRIC MOMENTS: APPLICATION TO THE REGISTRAION OF 3D SEGMENTED CT HEAD IMAGES. International Journal of Image and Graphics. 12(2). 1250009–1250009. 1 indexed citations
19.
Khlifa, Nawrès, et al.. (2009). A Based Bayesian Wavelet Thresholding Method to Enhance Nuclear Imaging. International Journal of Biomedical Imaging. 2009(1). 506120–506120. 26 indexed citations
20.
Khlifa, Nawrès, et al.. (2006). Image denoising using Wavelets: A powerful tool to overcome some limitations in nuclear imaging. 1. 1114–1118. 8 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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