Ines Njeh

637 total citations
25 papers, 417 citations indexed

About

Ines Njeh is a scholar working on Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence. According to data from OpenAlex, Ines Njeh has authored 25 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 14 papers in Neurology and 7 papers in Artificial Intelligence. Recurrent topics in Ines Njeh's work include Brain Tumor Detection and Classification (14 papers), Medical Image Segmentation Techniques (10 papers) and Advanced Neural Network Applications (9 papers). Ines Njeh is often cited by papers focused on Brain Tumor Detection and Classification (14 papers), Medical Image Segmentation Techniques (10 papers) and Advanced Neural Network Applications (9 papers). Ines Njeh collaborates with scholars based in Tunisia, Canada and France. Ines Njeh's co-authors include Hiba Mzoughi, Mohamed Ben Slima, Chokri Mhiri, Ali Wali, Ahmed Ben Hamida, Ismail Ben Ayed, Damien Galanaud, Stéphane Lehéricy, Stéphane Lehéricy and Wassim Zouch and has published in prestigious journals such as Biomedical Signal Processing and Control, Multimedia Tools and Applications and Applied Intelligence.

In The Last Decade

Ines Njeh

21 papers receiving 405 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ines Njeh Tunisia 9 303 286 131 122 40 25 417
Nelly Gordillo Mexico 5 364 1.2× 360 1.3× 124 0.9× 85 0.7× 43 1.1× 12 511
Hiba Mzoughi Tunisia 7 246 0.8× 217 0.8× 116 0.9× 98 0.8× 34 0.8× 19 325
Geethu Mohan India 4 246 0.8× 219 0.8× 92 0.7× 119 1.0× 33 0.8× 8 327
Syed M. S. Reza United States 9 302 1.0× 264 0.9× 124 0.9× 120 1.0× 32 0.8× 18 423
Rachida Saouli Algeria 5 235 0.8× 232 0.8× 107 0.8× 85 0.7× 38 0.9× 13 343
Ezequiel Geremia France 4 126 0.4× 197 0.7× 80 0.6× 69 0.6× 35 0.9× 6 303
Shuangliang Cao China 6 482 1.6× 435 1.5× 177 1.4× 264 2.2× 55 1.4× 11 639
Mostefa Ben Naceur Algeria 3 231 0.8× 223 0.8× 91 0.7× 78 0.6× 35 0.9× 3 311
Matthew C. Clark United States 5 265 0.9× 422 1.5× 120 0.9× 150 1.2× 30 0.8× 6 538

Countries citing papers authored by Ines Njeh

Since Specialization
Citations

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

Fields of papers citing papers by Ines Njeh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ines Njeh

This figure shows the co-authorship network connecting the top 25 collaborators of Ines Njeh. A scholar is included among the top collaborators of Ines Njeh 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 Ines Njeh. Ines Njeh 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.
Mzoughi, Hiba, et al.. (2025). An explainable AI for breast cancer classification using vision Transformer (ViT). Biomedical Signal Processing and Control. 108. 108011–108011.
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Mzoughi, Hiba, et al.. (2023). Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging. Multimedia Tools and Applications. 82(25). 39303–39325. 4 indexed citations
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Njeh, Ines, et al.. (2020). Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction. Medical & Biological Engineering & Computing. 59(1). 85–106. 4 indexed citations
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Mzoughi, Hiba, et al.. (2020). Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. Journal of Digital Imaging. 33(4). 903–915. 228 indexed citations
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Mzoughi, Hiba, et al.. (2020). Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures. Multimedia Tools and Applications. 80(1). 899–919. 13 indexed citations
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Njeh, Ines, et al.. (2020). Relative Ectopic Kidney Function Quantification Using DMSA Tomoscintigraphy Modality. Journal of Healthcare Engineering. 2020. 1–11. 1 indexed citations
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Mzoughi, Hiba, Ines Njeh, Mohamed Ben Slima, & Ahmed Ben Hamida. (2018). Histogram equalization-based techniques for contrast enhancement of MRI brain Glioma tumor images: Comparative study. 1–6. 21 indexed citations
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Njeh, Ines, et al.. (2018). Rank-Two NMF Clustering for Glioblastoma Characterization. Journal of Healthcare Engineering. 2018. 1–7. 1 indexed citations
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Njeh, Ines, et al.. (2016). An automated MRI brain tissue segmentation approach. 2 indexed citations
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Njeh, Ines, et al.. (2015). Towards a Computer Aided Prognosis for Brain Glioblastomas Tumor Growth Estimation. IEEE Transactions on NanoBioscience. 14(7). 727–733. 19 indexed citations
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Njeh, Ines, et al.. (2014). 3D multimodal MRI brain glioma tumor and edema segmentation: A graph cut distribution matching approach. Computerized Medical Imaging and Graphics. 40. 108–119. 41 indexed citations
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Njeh, Ines, et al.. (2014). A computer aided diagnosis ‘CAD’ for brain glioma exploration. 243–248. 4 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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