Semantic segmentation with attention dense U-net for lung extraction from 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:	Auvy, Akib Al Mahmud, Sharif, Shezhan, Chowdhury, Aseer Iqtider, Elahi, Mahbub-E, Mahmud, Washik Al
Other Authors:	Noor, Jannatun
Format:	Thesis
Language:	English
Published:	Brac University 2023
Subjects:	Lung Segmentation U-Net Dice coefficient IoU Attention dense U-net X-ray Lungs > Diseases Image processing > Digital techniques.
Online Access:	http://hdl.handle.net/10361/19449

id	10361-19449
record_format	dspace
spelling	10361-194492023-08-20T21:02:22Z Semantic segmentation with attention dense U-net for lung extraction from X-ray images Auvy, Akib Al Mahmud Sharif, Shezhan Chowdhury, Aseer Iqtider Elahi, Mahbub-E Mahmud, Washik Al Noor, Jannatun Department of Computer Science and Engineering, Brac University Lung Segmentation U-Net Dice coefficient IoU Attention dense U-net X-ray Lungs--Diseases Image processing--Digital techniques. 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 47-50). "In the diverse field of computer science, deep learning and digital image processing plays a vital role in medical image research. With a deep knowledge on hand, we can make a machine understand any medical documentation, and fourth, image segmentation, classification, detection, identification, and segmentation have become more reliable and precious by the day. Lung disease detection is one of the most challenging parts of automation machine detection; to achieve that, segmentation is vigorous. For our research purpose, we aim to seek a better-unused model for lung segmentation, and it is fruitless to justify all the deep learning model as the number is huge. Most of them has already been evaluated by another researcher. This is why we have used U-net architecture (Attention Dense U-Net, Dense U- Net, Attention U-Net, U-Net, U-Net++) to segment the lung from an X-ray image. For the named architecture, we have used two convolutional layers. Four types of accuracy measurement matrices were used to judge this U-Net model: accuracy, Dice coefficient, intersection over union(IoU), and validation loss. The milestone for our research is as follows: our collected dataset was originally 512 x 512 pixels which we converted to 256 x 256 pixels for a 2 x 2 patch. This enables the machine to read the image with a better result. The dataset is annotated and masked. After that, we performed deep learning of the U-Net structure for our dataset to train the U-Net model and segment lung pixels from an X-ray image. In this step, we first omitted the background from the image with the help of true positive, true negative, false positive, and false negative values. Finally, we measured our model accuracy by the advanced accuracy measurement algorithm to justify its capability in terms of unknown data. Following these three steps, we have found that Attention Dense U-Net gives the best accuracy for all given parameters, with the result of accuracy: 97.48% Dice coefficient: 94.87% IoU: 93.87%. And the lowest is base U-Net with an Accuracy score: of 96.68%, Dice Coefficient: of 92.1% IoU: of 91.75%. The study reflects that U-Net is unsuitable for the segmentation of lungs from X-ray images. Hence, we have suggested our approach with Attention Dense U-Net for lung segmentation." Akib Al Mahmud Auvy Shezhan Sharif Aseer Iqtider Chowdhury Mahbub-E-Elahi Washik Al Mahmud B. Computer Science 2023-08-20T05:14:16Z 2023-08-20T05:14:16Z 2023 2023-03 Thesis ID 18301169 ID 18301201 ID 18301182 ID 18101187 ID 18101179 http://hdl.handle.net/10361/19449 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. 50 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Lung Segmentation U-Net Dice coefficient IoU Attention dense U-net X-ray Lungs--Diseases Image processing--Digital techniques.
spellingShingle	Lung Segmentation U-Net Dice coefficient IoU Attention dense U-net X-ray Lungs--Diseases Image processing--Digital techniques. Auvy, Akib Al Mahmud Sharif, Shezhan Chowdhury, Aseer Iqtider Elahi, Mahbub-E Mahmud, Washik Al Semantic segmentation with attention dense U-net for lung extraction from 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	Noor, Jannatun
author_facet	Noor, Jannatun Auvy, Akib Al Mahmud Sharif, Shezhan Chowdhury, Aseer Iqtider Elahi, Mahbub-E Mahmud, Washik Al
format	Thesis
author	Auvy, Akib Al Mahmud Sharif, Shezhan Chowdhury, Aseer Iqtider Elahi, Mahbub-E Mahmud, Washik Al
author_sort	Auvy, Akib Al Mahmud
title	Semantic segmentation with attention dense U-net for lung extraction from X-ray images
title_short	Semantic segmentation with attention dense U-net for lung extraction from X-ray images
title_full	Semantic segmentation with attention dense U-net for lung extraction from X-ray images
title_fullStr	Semantic segmentation with attention dense U-net for lung extraction from X-ray images
title_full_unstemmed	Semantic segmentation with attention dense U-net for lung extraction from X-ray images
title_sort	semantic segmentation with attention dense u-net for lung extraction from x-ray images
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19449
work_keys_str_mv	AT auvyakibalmahmud semanticsegmentationwithattentiondenseunetforlungextractionfromxrayimages AT sharifshezhan semanticsegmentationwithattentiondenseunetforlungextractionfromxrayimages AT chowdhuryaseeriqtider semanticsegmentationwithattentiondenseunetforlungextractionfromxrayimages AT elahimahbube semanticsegmentationwithattentiondenseunetforlungextractionfromxrayimages AT mahmudwashikal semanticsegmentationwithattentiondenseunetforlungextractionfromxrayimages
_version_	1814309218236235776

Semantic segmentation with attention dense U-net for lung extraction from X-ray images

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