Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Detalhes bibliográficos
Main Authors:	Reza, Jamil Ur, Tannee, Khadiza Siddique, Orpa, Sanjana Maruf, Fahim, Hasan Mahmud, Foysal, Riyad
Outros Autores:	Shakil, Arif
Formato:	Thesis
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	CNN ConvNeXt GoogleNet AlexNet ResNet50 Neural networks (Computer science) Image processing
Acesso em linha:	http://hdl.handle.net/10361/18308

id	10361-18308
record_format	dspace
spelling	10361-183082023-05-23T21:01:55Z Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer Reza, Jamil Ur Tannee, Khadiza Siddique Orpa, Sanjana Maruf Fahim, Hasan Mahmud Foysal, Riyad Shakil, Arif Department of Computer Science and Engineering, Brac University CNN ConvNeXt GoogleNet AlexNet ResNet50 Neural networks (Computer science) Image processing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-32). One of the most devastating cancers in the world is lung cancer. It is estimated that nearly a third of the world’s cancer fatalities are due to lung cancer. Diagnosis and treatment of primary and metastatic cancers depend heavily on the ability to identify and characterize malignant cells. On the other hand, early detection of lung cancer is crucial for a patient’s survival and significantly increases the survival rate. Malignant lung nodules may be detected early by oncologists using a variety of diagnostic methods such as needle prick biopsy and other types of imaging tests such as CT and PET scanning, as well as clinical examinations and other types of imaging tests. It’s important to note that these treatments and biopsies are risky. A higher proportion of people are being infected with the disease, on the other hand. CT scans are commonly performed in the early stages of cancer detection. Lung cancer may be detected with a 2.6 to 10-fold higher CT detection rate than analog radiography, according to Awai [1]. As the slices get thinner, so does their ability to recognize objects accurately. To evaluate one slice, radiologists need an average of two to three minutes. The burden of cancer patients is increasing as the number of those diagnosed grows. CT imaging may be used to detect malignancy and cancerous nodules in a patient. When cancer nodules (stage I) are discovered, treatment may begin, and the danger of cancer spreading can be minimized. 70 percent to 92 percent of people diagnosed with stage 1 non-small cell lung cancer (NSCLC) should expect to live for at least five years following their diagnosis, according to existing statistics[30] . Considering the fact that a large number of early detection methods are already available, further research is needed to improve the accuracy of these methods and, as a consequence, the overall survival rate. Using ConvNeXt, we believe we can work more efficiently and precisely. Radiologists will also benefit greatly from this change. The validity of the proposed network was evaluated by comparing its performance to that of the other pre-trained CNNs, such as GoogleNet, AlexNet, and ResNet50, using a simulated dataset of pre-processed CT scan images: the Luna 16 dataset. Since our network outperforms the other networks in terms of classification, accuracy is evident from the results. Aside from pulmonary nodule detection, this proposal’s approach may be simply adjusted to conduct classification jobs on any 3D medical diagnostic computed tomography pictures where the classification is very unpredictable and ambiguous, such as any other 3D medical diagnostic CT images. Jamil Ur Reza Khadiza Siddique Tannee Sanjana Maruf Orpa Hasan Mahmud Fahim Riyad Foysal B. Computer Science 2023-05-23T05:18:51Z 2023-05-23T05:18:51Z 2022 2022-05 Thesis ID 18101693 ID 18101260 ID 18101476 ID 18101318 ID 18101559 http://hdl.handle.net/10361/18308 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. 32 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	CNN ConvNeXt GoogleNet AlexNet ResNet50 Neural networks (Computer science) Image processing
spellingShingle	CNN ConvNeXt GoogleNet AlexNet ResNet50 Neural networks (Computer science) Image processing Reza, Jamil Ur Tannee, Khadiza Siddique Orpa, Sanjana Maruf Fahim, Hasan Mahmud Foysal, Riyad Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Shakil, Arif
author_facet	Shakil, Arif Reza, Jamil Ur Tannee, Khadiza Siddique Orpa, Sanjana Maruf Fahim, Hasan Mahmud Foysal, Riyad
format	Thesis
author	Reza, Jamil Ur Tannee, Khadiza Siddique Orpa, Sanjana Maruf Fahim, Hasan Mahmud Foysal, Riyad
author_sort	Reza, Jamil Ur
title	Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
title_short	Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
title_full	Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
title_fullStr	Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
title_full_unstemmed	Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
title_sort	unmasking malignancy of lung nodule using a modernized convnet toward the design of a vision transformer
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/18308
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Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer

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