Comparison of deep transfer learning models for cancer diagnosis

Comparison of deep transfer learning models for cancer diagnosis

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

Dettagli Bibliografici
Autori principali: Joya, Nadia Islam, Turna, Tasfia Haque, Sukhi, Zinia Nawrin, Promy, Tania Ferdousey
Altri autori: Uddin, Jia
Natura: Tesi
Lingua:English
Pubblicazione: Brac University 2023
Soggetti:
Convolutional Neural Network(CNN)
Deep transfer learning
Cancer detection
Image processing
Neural networks (Computer science)
Image processing > Digital techniques.
Accesso online:http://hdl.handle.net/10361/18250
id 10361-18250
record_format dspace
spelling 10361-182502023-05-09T21:01:56Z Comparison of deep transfer learning models for cancer diagnosis Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Promy, Tania Ferdousey Uddin, Jia Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Convolutional Neural Network(CNN) Deep transfer learning Cancer detection Image processing Neural networks (Computer science) Image processing--Digital techniques. 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 51-56). Cancer is known to be one of the most lethal diseases among all the diseases in the world. It is clinically known as ’Malignant Neoplasm which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. According to GLOBOCAN 2020, approximately 19.3 million new cases were found and around 10.0 million deaths have occurred for cancer globally. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. Breast, colorectal, endometrial, lung, oral, skin, and ovarian cancers are some of the most common malignancies that people develop. There are many medical procedures to identify the cancer cell such as mammography, MRI, CT scan which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of deep learning in medical image classification tasks, the recommended initiative targets to analyze the performance of deep transfer learning for cancer cell classification. Transfer learning is used in visual categorization to solve cross-domain learning issues by transferring useful data from the source domain to the task domain. Cancer, also known as tumor, must be discovered early and accurately in order to determine what treatment alternatives are available. Even if each modality has its own set of problems, such as a convoluted medical history, incorrect diagnosis, and therapy, all of which are major causes of death. Artificial Intelligence-based medical diagnosis is a novel strategy in medicine that eliminates the need for pathologists to work with material in favor of pixels to diagnose illness (imaging in medical sector). Therefore, in our paper, we want to offer a narrative of four different deep transfer learning techniques Vgg16, InceptionV3, MobilenetV2 and Resnet50 to examine the accuracy, compare and discuss for the detection of breast, lung, and melanoma (skin) cancer. Nadia Islam Joya Tasfia Haque Turna Zinia Nawrin Sukhi Tania Ferdousey Promy B. Computer Science 2023-05-09T05:23:54Z 2023-05-09T05:23:54Z 2022 2022-05 Thesis ID 18101105 ID 18301280 ID 18301193 ID 18101678 http://hdl.handle.net/10361/18250 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. 56 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic Convolutional Neural Network(CNN)
Deep transfer learning
Cancer detection
Image processing
Neural networks (Computer science)
Image processing--Digital techniques.
spellingShingle Convolutional Neural Network(CNN)
Deep transfer learning
Cancer detection
Image processing
Neural networks (Computer science)
Image processing--Digital techniques.
Joya, Nadia Islam
Turna, Tasfia Haque
Sukhi, Zinia Nawrin
Promy, Tania Ferdousey
Comparison of deep transfer learning models for cancer diagnosis
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2 Uddin, Jia
author_facet Uddin, Jia
Joya, Nadia Islam
Turna, Tasfia Haque
Sukhi, Zinia Nawrin
Promy, Tania Ferdousey
format Thesis
author Joya, Nadia Islam
Turna, Tasfia Haque
Sukhi, Zinia Nawrin
Promy, Tania Ferdousey
author_sort Joya, Nadia Islam
title Comparison of deep transfer learning models for cancer diagnosis
title_short Comparison of deep transfer learning models for cancer diagnosis
title_full Comparison of deep transfer learning models for cancer diagnosis
title_fullStr Comparison of deep transfer learning models for cancer diagnosis
title_full_unstemmed Comparison of deep transfer learning models for cancer diagnosis
title_sort comparison of deep transfer learning models for cancer diagnosis
publisher Brac University
publishDate 2023
url http://hdl.handle.net/10361/18250
work_keys_str_mv AT joyanadiaislam comparisonofdeeptransferlearningmodelsforcancerdiagnosis
AT turnatasfiahaque comparisonofdeeptransferlearningmodelsforcancerdiagnosis
AT sukhizinianawrin comparisonofdeeptransferlearningmodelsforcancerdiagnosis
AT promytaniaferdousey comparisonofdeeptransferlearningmodelsforcancerdiagnosis
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