dc.contributor.advisor | Hossain, Muhammad Iqbal | |
dc.contributor.advisor | Reza, Md. Tanzim | |
dc.contributor.author | Datta, Nirjhor | |
dc.contributor.author | Rashid, Md. Hasanur | |
dc.contributor.author | Rahman, Samiur | |
dc.contributor.author | Nodi, Naima Tahsin | |
dc.contributor.author | Uddin, Moin | |
dc.date.accessioned | 2024-05-15T06:48:58Z | |
dc.date.available | 2024-05-15T06:48:58Z | |
dc.date.copyright | ©2024 | |
dc.date.issued | 2024-01 | |
dc.identifier.other | ID: 20101540 | |
dc.identifier.other | ID: 23241144 | |
dc.identifier.other | ID: 20101147 | |
dc.identifier.other | ID: 20101150 | |
dc.identifier.other | ID: 20101134 | |
dc.identifier.uri | http://hdl.handle.net/10361/22838 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 40-43). | |
dc.description.abstract | 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. | en_US |
dc.description.statementofresponsibility | Nirjhor Datta | |
dc.description.statementofresponsibility | Md. Hasanur Rashid | |
dc.description.statementofresponsibility | Samiur Rahman | |
dc.description.statementofresponsibility | Naima Tahsin Nodi | |
dc.description.statementofresponsibility | Moin Uddin | |
dc.format.extent | 54 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | 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. | |
dc.subject | Deep learning | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | CNN | en_US |
dc.subject | Adrenocortical Carcinoma | en_US |
dc.subject | Disease detection | en_US |
dc.subject.lcsh | Neural networks (Computer science) | |
dc.subject.lcsh | Deep learning (Machine learning) | |
dc.subject.lcsh | Computational intelligence | |
dc.title | Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B.Sc in Computer Science and Engineering | |