An efficient deep learning approach to detect skin Cancer

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

书目详细资料
Main Authors:	Islam, Ashfaqul, Khan, Daiyan, Chowdhury, Rakeen Ashraf
其他作者:	Alam, Md. Ashraful
格式:	Thesis
语言:	English
出版:	Brac University 2022
主题:	Cancer detection Convolutional neural networks Image classification Deep learning Machine learning Cognitive learning theory (Deep learning)
在线阅读:	http://hdl.handle.net/10361/15932

id	10361-15932
record_format	dspace
spelling	10361-159322022-01-26T10:16:00Z An efficient deep learning approach to detect skin Cancer Islam, Ashfaqul Khan, Daiyan Chowdhury, Rakeen Ashraf Alam, Md. Ashraful Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Cancer detection Convolutional neural networks Image classification Deep learning Machine learning Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 52-53). Each year, millions of people around the world are affected by cancer. Research shows that the early and accurate diagnosis of cancerous growths can have a major effect on improving mortality rates from cancer. As human diagnosis is prone to error, a deep-learning based computerized diagnostic system should be considered. In our research, we tackled the issues caused by difficulties in diagnosing skin cancer and distinguishing between different types of skin growths, especially without the use of advanced medical equipment and a high level of medical expertise of the diagnosticians. To do so, we have implemented a system that will use a deep-learning approach to be able to detect skin cancer from digital images. This paper discusses the identification of cancer from 7 different types of skin lesions from images using CNN with Keras Sequential API. We have used the publicly available HAM10000 dataset, obtained from the Harvard Dataverse. This dataset contains 10,015 labeled images of skin growths. We applied multiple data pre-processing methods after reading the data and before training our model. For accuracy checks and as a means of comparison we have pre-trained data, using ResNet50, DenseNet121, and VGG11, some well-known transfer learning models. This helps identify better methods of machine-learning application in the field of skin growth classification for skin cancer detection. Our model achieved an accuracy of over 97% in the proper identification of the type of skin growth. Ashfaqul Islam Daiyan Khan Rakeen Ashraf Chowdhury B. Computer Science 2022-01-17T04:20:34Z 2022-01-17T04:20:34Z 2021 2021-09 Thesis ID 20341030 ID 19141024 ID 16141014 http://hdl.handle.net/10361/15932 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. 53 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Cancer detection Convolutional neural networks Image classification Deep learning Machine learning Cognitive learning theory (Deep learning)
spellingShingle	Cancer detection Convolutional neural networks Image classification Deep learning Machine learning Cognitive learning theory (Deep learning) Islam, Ashfaqul Khan, Daiyan Chowdhury, Rakeen Ashraf An efficient deep learning approach to detect skin Cancer
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Alam, Md. Ashraful
author_facet	Alam, Md. Ashraful Islam, Ashfaqul Khan, Daiyan Chowdhury, Rakeen Ashraf
format	Thesis
author	Islam, Ashfaqul Khan, Daiyan Chowdhury, Rakeen Ashraf
author_sort	Islam, Ashfaqul
title	An efficient deep learning approach to detect skin Cancer
title_short	An efficient deep learning approach to detect skin Cancer
title_full	An efficient deep learning approach to detect skin Cancer
title_fullStr	An efficient deep learning approach to detect skin Cancer
title_full_unstemmed	An efficient deep learning approach to detect skin Cancer
title_sort	efficient deep learning approach to detect skin cancer
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/15932
work_keys_str_mv	AT islamashfaqul anefficientdeeplearningapproachtodetectskincancer AT khandaiyan anefficientdeeplearningapproachtodetectskincancer AT chowdhuryrakeenashraf anefficientdeeplearningapproachtodetectskincancer AT islamashfaqul efficientdeeplearningapproachtodetectskincancer AT khandaiyan efficientdeeplearningapproachtodetectskincancer AT chowdhuryrakeenashraf efficientdeeplearningapproachtodetectskincancer
_version_	1814308620469272576

An efficient deep learning approach to detect skin Cancer

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