Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.

Detalhes bibliográficos
Principais autores:	Datta, Nirjhor, Rashid, Md. Hasanur, Rahman, Samiur, Nodi, Naima Tahsin, Uddin, Moin
Outros Autores:	Hossain, Muhammad Iqbal
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2024
Assuntos:	Deep learning Convolutional neural network CNN Adrenocortical Carcinoma Disease detection Neural networks (Computer science) Deep learning (Machine learning) Computational intelligence
Acesso em linha:	http://hdl.handle.net/10361/22838

id	10361-22838
record_format	dspace
spelling	10361-228382024-05-15T21:02:14Z Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification Datta, Nirjhor Rashid, Md. Hasanur Rahman, Samiur Nodi, Naima Tahsin Uddin, Moin Hossain, Muhammad Iqbal Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Deep learning Convolutional neural network CNN Adrenocortical Carcinoma Disease detection Neural networks (Computer science) Deep learning (Machine learning) Computational intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-43). Adrenocortical Carcinoma (ACC) is a rare but highly lethal cancer that occurs in the adrenal cortex. Accurate diagnosis of ACC are vital in order to determine appropriate treatment strategies and predict patient outcomes. Hence, defining the stages of ACC is a crucial factor for both diagnosis and treatment planning and it is the key aspect that the researchers are still exploring. Our study proposes a novel deep learning-based hybrid Multi-Task model which performs both segmentation to find the exact cancer region and classification based on the cancer stages. Thus our model is resource efficient. In our research, several deep learning-based architectures have been used to segment and evaluate the ACC CT images. Moreover, we have explored how Convolutional Neural Network (CNN) classification models perform on the classification task. This process includes the exploration to find the model based on the Multi-Task learning model’s feature extraction perform on classification task. Nirjhor Datta Md. Hasanur Rashid Samiur Rahman Naima Tahsin Nodi Moin Uddin B.Sc in Computer Science and Engineering 2024-05-15T06:48:58Z 2024-05-15T06:48:58Z ©2024 2024-01 Thesis ID: 20101540 ID: 23241144 ID: 20101147 ID: 20101150 ID: 20101134 http://hdl.handle.net/10361/22838 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. 54 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Convolutional neural network CNN Adrenocortical Carcinoma Disease detection Neural networks (Computer science) Deep learning (Machine learning) Computational intelligence
spellingShingle	Deep learning Convolutional neural network CNN Adrenocortical Carcinoma Disease detection Neural networks (Computer science) Deep learning (Machine learning) Computational intelligence Datta, Nirjhor Rashid, Md. Hasanur Rahman, Samiur Nodi, Naima Tahsin Uddin, Moin Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Datta, Nirjhor Rashid, Md. Hasanur Rahman, Samiur Nodi, Naima Tahsin Uddin, Moin
format	Thesis
author	Datta, Nirjhor Rashid, Md. Hasanur Rahman, Samiur Nodi, Naima Tahsin Uddin, Moin
author_sort	Datta, Nirjhor
title	Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
title_short	Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
title_full	Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
title_fullStr	Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
title_full_unstemmed	Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
title_sort	deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22838
work_keys_str_mv	AT dattanirjhor deeplearningbasedhybridmultitaskmodelforadrenocorticalcarcinomasegmentationandclassification AT rashidmdhasanur deeplearningbasedhybridmultitaskmodelforadrenocorticalcarcinomasegmentationandclassification AT rahmansamiur deeplearningbasedhybridmultitaskmodelforadrenocorticalcarcinomasegmentationandclassification AT nodinaimatahsin deeplearningbasedhybridmultitaskmodelforadrenocorticalcarcinomasegmentationandclassification AT uddinmoin deeplearningbasedhybridmultitaskmodelforadrenocorticalcarcinomasegmentationandclassification
_version_	1814308269439582208

Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification

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