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.

Bibliographic Details
Main Authors:	Bin Mushfiq, Rahil, Zannah, Rafiatul, Bashar, Mubtasim, Alam, Md. Nafidul, Rahman, MD Aftabur
Other Authors:	Chakrabarty, Amitabha
Format:	Thesis
Language:	English
Published:	Brac University 2024
Subjects:	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)
Online Access:	http://hdl.handle.net/10361/22671

id	10361-22671
record_format	dspace
spelling	10361-226712024-04-24T21:06:08Z A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images Bin Mushfiq, Rahil Zannah, Rafiatul Bashar, Mubtasim Alam, Md. Nafidul Rahman, MD Aftabur Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University 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) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 77-80). Digital image processing utilizes deep learning to tackle challenging issues such as image colourization, classification, segmentation, and detection. The medical image analysis field is developing day by day, and segmenting organs, diseases, or abnor malities is a challenging task to complete. Dental disease diagnosis is one of these fields where image segmentation can help gain significant improvements as dentists worldwide face various problems in diagnosing dental diseases with the naked eye. Compared to other medical images, dental radiographic images provide multiple challenges in terms of processing, making segmentation a more complex task. Deep neural network models are used more frequently for various image segmentation ap plications. U-Net is one such model. Multiple variations and advancements have been created for this network model to serve better performance, mainly on seman tic segmentation of medical images. However, comparative studies must determine how well these variants perform in segmenting dental x-ray images. This research uses six U-Net architecture (Vanilla U-net, Dense U-net, Attention U-net, SE U net, Residual U-net, R2 U-net) variants for segmenting dental radiographic X-rays that are extensively and effectively compared. Some U-Net architectural variations under consideration still need to be evaluated for segmenting dental radiographic X-rays. For all architectures, we used 2 and 3 convolutional layers. We used four types of matrices to compare the models: Accuracy, Dice coefficient, F1 score and IoU. Among the variants, Vanilla-unet with two convolutional layers provided the best Accuracy of 95.56% and IoU score of 88% on the validation set for much lesser time than other architectures. On the other hand, when we use three convolutional layers, dense-unet provides the best Accuracy of 95.94% and IoU score of 89.07% on the validation set. However, most of the examined architectures throughout the dataset showed minor changes when segmentation performance was measured using all four accuracy metrics. This study indicates that U-Net is enough for radio graphic X-ray segmentation. Choosing simpler models will save time and money during testing and model creation. Therefore, our suggested approach might aid in making automated dental disease diagnosis models. Rahil Bin Mushfiq Rafiatul Zannah Mubtasim Bashar Md. Nafidul Alam MD Aftabur Rahman B.Sc. in Computer Science 2024-04-24T08:58:48Z 2024-04-24T08:58:48Z 2023 2023-01 Thesis ID: 18101552 ID: 18301027 ID: 18301046 ID: 18101080 ID: 18101071 http://hdl.handle.net/10361/22671 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 80 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	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)
spellingShingle	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) Bin Mushfiq, Rahil Zannah, Rafiatul Bashar, Mubtasim Alam, Md. Nafidul Rahman, MD Aftabur A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Bin Mushfiq, Rahil Zannah, Rafiatul Bashar, Mubtasim Alam, Md. Nafidul Rahman, MD Aftabur
format	Thesis
author	Bin Mushfiq, Rahil Zannah, Rafiatul Bashar, Mubtasim Alam, Md. Nafidul Rahman, MD Aftabur
author_sort	Bin Mushfiq, Rahil
title	A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
title_short	A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
title_full	A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
title_fullStr	A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
title_full_unstemmed	A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
title_sort	comparison of deep learning u‐net architectures for semantic segmentation on panoramic x-ray images
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22671
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A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images

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