Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms

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

书目详细资料
Main Authors:	Al Mamun, Rafsan, Rafin, Gazi Abu, Alam, Adnan, Sefat, MD. Al Imran
其他作者:	Bin Ashraf, Faisal
格式:	Thesis
语言:	en_US
出版:	Brac University 2022
主题:	Breast cancer Malignant Benign Mammogram CAD model Convolutional neural network Convolution layer Overfitting MIAS database Accuracy Precision Recall F1 ROC Curve AUC Neural networks (Computer science) Early Detection of Cancer > methods.
在线阅读:	http://hdl.handle.net/10361/17172

id	10361-17172
record_format	dspace
spelling	10361-171722022-09-07T21:01:39Z Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms Al Mamun, Rafsan Rafin, Gazi Abu Alam, Adnan Sefat, MD. Al Imran Bin Ashraf, Faisal Mostakim, Moin Department of Computer Science and Engineering, Brac University Breast cancer Malignant Benign Mammogram CAD model Convolutional neural network Convolution layer Overfitting MIAS database Accuracy Precision Recall F1 ROC Curve AUC Neural networks (Computer science) Early Detection of Cancer--methods. 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 47-51). Cancer, a diagnosis so dreaded and scary, that its fear alone can strike even the strongest of souls. The disease is often thought of as untreatable and unbearably painful, with usually, no cure available. Among all the cancers, breast cancer is the second most deadliest , especially among women. What decides the patients’ fate is the early diagnosis of the cancer, facilitating subsequent clinical management. Mammography plays a vital role in the screening of breast cancers as it can detect any breast masses or calcifications early. However, the extremely dense breast tissues pose difficulty in the detection of cancer mass, thus, encouraging the use of machine learning (ML) techniques and artificial neural networks (ANN) to assist radiologists in faster cancer diagnosis. This paper explores the MIAS database, containing 332 digital mammograms from women, which were augmented and preprocessed, and fed into a custom and different pre-trained convolutional neural network (CNN) models, with the aim of differentiating healthy tissues from cancerous ones with high accuracy. Although the pre-trained CNN models produced splendid results, the custom CNN model came out on top, achieving test accuracy, AUC, precision, recall and F1 scores of 0.9362, 0.9407, 0.9200, 0.8025 and 0.8572 respectively while having minimal to no overfitting. The paper, along with proposing a new custom CNN model for better breast cancer classification using raw mammograms, focuses on the significance of computer-aided detection (CAD) models overall in the early diagnosis of breast cancer. While a diagnosis of breast cancer may still leave patients dreaded, we believe our research can be a symbol of hope for all. Rafsan Al Mamun Gazi Abu Rafin Adnan Alam MD. Al Imran Sefat B. Computer Science 2022-09-07T10:16:25Z 2022-09-07T10:16:25Z 2022 2022-01 Thesis ID: 18301033 ID: 21241072 ID: 21241071 ID: 21241076 http://hdl.handle.net/10361/17172 en_US 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. 51 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Breast cancer Malignant Benign Mammogram CAD model Convolutional neural network Convolution layer Overfitting MIAS database Accuracy Precision Recall F1 ROC Curve AUC Neural networks (Computer science) Early Detection of Cancer--methods.
spellingShingle	Breast cancer Malignant Benign Mammogram CAD model Convolutional neural network Convolution layer Overfitting MIAS database Accuracy Precision Recall F1 ROC Curve AUC Neural networks (Computer science) Early Detection of Cancer--methods. Al Mamun, Rafsan Rafin, Gazi Abu Alam, Adnan Sefat, MD. Al Imran Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Bin Ashraf, Faisal
author_facet	Bin Ashraf, Faisal Al Mamun, Rafsan Rafin, Gazi Abu Alam, Adnan Sefat, MD. Al Imran
format	Thesis
author	Al Mamun, Rafsan Rafin, Gazi Abu Alam, Adnan Sefat, MD. Al Imran
author_sort	Al Mamun, Rafsan
title	Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms
title_short	Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms
title_full	Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms
title_fullStr	Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms
title_full_unstemmed	Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms
title_sort	application of deep convolutional neural network in breast cancer prediction using digital mammograms
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17172
work_keys_str_mv	AT almamunrafsan applicationofdeepconvolutionalneuralnetworkinbreastcancerpredictionusingdigitalmammograms AT rafingaziabu applicationofdeepconvolutionalneuralnetworkinbreastcancerpredictionusingdigitalmammograms AT alamadnan applicationofdeepconvolutionalneuralnetworkinbreastcancerpredictionusingdigitalmammograms AT sefatmdalimran applicationofdeepconvolutionalneuralnetworkinbreastcancerpredictionusingdigitalmammograms
_version_	1814309055865290752

Application of deep convolutional neural network in breast cancer prediction using Digital Mammograms

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