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

Internet

http://hdl.handle.net/10361/22671

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