TARIK ZOUAGUI
Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, University of Science and Technology of Oran (USTO-MB), National Institute of Applied Sciences
0000-0003-1631-2976
About
Tarik ZOUAGUI received the engineering degree in electronics and the Magister degree (Signal and Image Processing) both from the University of Science and Technology of Oran (USTO), Algeria. He received also a PhD in Image and Systems from INSA de Lyon, France in 2004. He is currently Full Professor at USTO, at the electronics department. His teaching activities mainly concern signal and image processing, and medical imaging. His research interests are in the area of image processing with particular focus on image segmentation, medical image analysis with deep learning, and remote sensing.
Employment
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Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf Full Professor2006 - Present
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University of Science and Technology of Oran (USTO-MB) Prof.2006 - Present
Education
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National Institute of Applied Sciences PhD2000 - 2004
Projects & Funding
Projects & funding information is unavailable.
Publications (22)
- GeoLinkNet: A Geometry-Aware Deep Learning Model for Road Extraction From Satellite Imagery Save
- From Satellite to Shapefiles: <scp>ZymbaNet</scp> For Memory‐Efficient Automated Road Mapping Save
- Advancing dermoscopic system for skin lesion segmentation and classification: a novel deep attention residual UNet empowered by ECA net blocks Save
- Conversational Algerian Sign Language Recognition Using the Deep Learning Approach Save
- U-SPIN Pyramid: U-Net with Spatial and Interaction Space Graph Reasoning for Improving Road Extraction from Satellite Images Save
- Analysis of Polyp Segmentation in Colonoscopy Imaging: A Comparative Study with U2Net Save
- Enhancing Building Extraction from Remote Sensing Images Using YOLOV8 Save
- Enhancing Skin Lesions Segmentation and Classification through Hierarchical Attention ResUnet Model Save
- Ultrasound Segmentation of Thyroid Nodules: An Enhancement Deep Neural Network Framework Save
- Improved Skin Lesion Segmentation and Classification using Hierarchical Deep Attention Architectures Save