Thoracic complication detection on chest X-rays using deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí:	Ratul, Rizwanul Hoque, Husain, Farah Anjum, Purnata, Tajmim Hossain, Pomil, Rifat Alam, Khandoker, Shaima
Rannpháirtithe:	Parvez, Zavid
Foilsithe / Cruthaithe:	2021
Ábhair:	Deep learning Chest X-ray Convolutional Neural Networks Thoracic diseases DenseNet X-rays

id	10361-14993
record_format	dspace
spelling	10361-149932022-01-26T10:04:54Z Thoracic complication detection on chest X-rays using deep learning Ratul, Rizwanul Hoque Husain, Farah Anjum Purnata, Tajmim Hossain Pomil, Rifat Alam Khandoker, Shaima Parvez, Zavid Department of Computer Science and Engineering, Brac University Deep learning Chest X-ray Convolutional Neural Networks Thoracic diseases DenseNet X-rays This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 27-29). Respiratory or thoracic diseases are one of the leading causes of death worldwide. Many of these diseases can be treated and prevented with proper diagnosis and early care. The diagnosis of thoracic diseases is mainly done with t. X-rays and other chest imaging techniques and making sense of these require expert radiologists, who aren't always accessible. Especially in underdeveloped countries, a lot of patients with thoracic diseases are left to die due to a lack of proper diagnosis. With the advent of Artificial Intelligence, there have been many initiatives trying to tackle these medical diagnosis problems through the use of n techniques and pattern recognition. Using Deep Neural Networks, patterns corresponding to different thoracic diseases can be easily detected from chest X-rays. In this paper, we have proposed just such a model that can identify the presence of a disease from a range of 14 different thoracic diseases using a Dense Convolutional Neural Network. Our dense convolutional neural network model takes advantage of the sheer amount of data that is now publicly available from chest X-ray datasets. This novel approach works in 2 stages, first training on images from disease-ridden patients alone, and then training the entire network on the whole dataset which includes X-rays from both healthy and unhealthy patients. This allows the model to make better predictions in detecting the presence of diseases as well as the absence. Given a chest X-ray alone, our model can give accurate predictions, with an AUROC mean score of 82.9% competing with the existing state-of-the-art models in this field. Rizwanul Hoque Ratul Farah Anjum Husain Tajmim Hossain Purnata Rifat Alam Pomil Shaima Khandoker B. Computer Science 2021-09-09T12:38:31Z 2021-09-09T12:38:31Z 2021 2021-06 ID 17101079 ID 17101166 ID 17101146 ID 17101284 17101003 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. 29 pages application/pdf
institution	Brac University
collection	Institutional Repository
topic	Deep learning Chest X-ray Convolutional Neural Networks Thoracic diseases DenseNet X-rays
spellingShingle	Deep learning Chest X-ray Convolutional Neural Networks Thoracic diseases DenseNet X-rays Ratul, Rizwanul Hoque Husain, Farah Anjum Purnata, Tajmim Hossain Pomil, Rifat Alam Khandoker, Shaima Thoracic complication detection on chest X-rays using deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Parvez, Zavid
author_facet	Parvez, Zavid Ratul, Rizwanul Hoque Husain, Farah Anjum Purnata, Tajmim Hossain Pomil, Rifat Alam Khandoker, Shaima
author	Ratul, Rizwanul Hoque Husain, Farah Anjum Purnata, Tajmim Hossain Pomil, Rifat Alam Khandoker, Shaima
author_sort	Ratul, Rizwanul Hoque
title	Thoracic complication detection on chest X-rays using deep learning
title_short	Thoracic complication detection on chest X-rays using deep learning
title_full	Thoracic complication detection on chest X-rays using deep learning
title_fullStr	Thoracic complication detection on chest X-rays using deep learning
title_full_unstemmed	Thoracic complication detection on chest X-rays using deep learning
title_sort	thoracic complication detection on chest x-rays using deep learning
publishDate	2021
work_keys_str_mv	AT ratulrizwanulhoque thoraciccomplicationdetectiononchestxraysusingdeeplearning AT husainfarahanjum thoraciccomplicationdetectiononchestxraysusingdeeplearning AT purnatatajmimhossain thoraciccomplicationdetectiononchestxraysusingdeeplearning AT pomilrifatalam thoraciccomplicationdetectiononchestxraysusingdeeplearning AT khandokershaima thoraciccomplicationdetectiononchestxraysusingdeeplearning
_version_	1814306992284499968

Thoracic complication detection on chest X-rays using deep learning

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