A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images

A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Dades bibliogràfiques
Autors principals:	Bin Mushfiq, Rahil, Zannah, Rafiatul, Bashar, Mubtasim, Alam, Md. Nafidul, Rahman, MD Aftabur
Altres autors:	Chakrabarty, Amitabha
Format:	Thesis
Idioma:	English
Publicat:	Brac University 2024
Matèries:	Dental Semantic segmentation Data annotation OPG image U-net U-net variants Dice coefficient IoU Architecture comparison Deep learning (Machine learning) Cognitive learning theory (Deep learning)
Accés en línia:	http://hdl.handle.net/10361/22671

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