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.

Podrobná bibliografie
Hlavní autoři:	Bin Mushfiq, Rahil, Zannah, Rafiatul, Bashar, Mubtasim, Alam, Md. Nafidul, Rahman, MD Aftabur
Další autoři:	Chakrabarty, Amitabha
Médium:	Diplomová práce
Jazyk:	English
Vydáno:	Brac University 2024
Témata:	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)
On-line přístup:	http://hdl.handle.net/10361/22671

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