CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK

Early diagnosis of some diseases can be obtained from some technologies developed recent years. One of the famous techniques used in medical field is using Neural Networks to discriminate and classify raw images of some diseases into their respective type and stages. This project is continuation fro...

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Main Author: IBRAHIM, NURUL AIDA RADHIAH
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.19087 /
Published: 2017
Online Access: http://utpedia.utp.edu.my/19087/1/%5BFYP%202%5D%20Dissertation%20Report.pdf
http://utpedia.utp.edu.my/19087/
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spelling utp-utpedia.190872019-06-20T11:05:45Z http://utpedia.utp.edu.my/19087/ CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK IBRAHIM, NURUL AIDA RADHIAH Early diagnosis of some diseases can be obtained from some technologies developed recent years. One of the famous techniques used in medical field is using Neural Networks to discriminate and classify raw images of some diseases into their respective type and stages. This project is continuation from previous FYP student which has done the same simulation but using different type of classifier and filter. However, in recent literature review found, it is said that using Neural Network method is more likely to have higher performance and accuracy compared to other methods. Thus, in this paper we propose to use Convolutional Neural Network (CNN) in the classification of Emphysema using a group of dataset provided. This report includes the background of emphysema and problems related to our objectives. Second chapter will discuss the previous study done by other researches related to CNN and lung diseases. Methodology and results obtained are shared in chapter three and four respectively. Some of the recommendation and references are listed at the end of this report. 2017-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/19087/1/%5BFYP%202%5D%20Dissertation%20Report.pdf IBRAHIM, NURUL AIDA RADHIAH (2017) CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK. UNSPECIFIED.
institution Universiti Teknologi Petronas
collection UTPedia
language English
description Early diagnosis of some diseases can be obtained from some technologies developed recent years. One of the famous techniques used in medical field is using Neural Networks to discriminate and classify raw images of some diseases into their respective type and stages. This project is continuation from previous FYP student which has done the same simulation but using different type of classifier and filter. However, in recent literature review found, it is said that using Neural Network method is more likely to have higher performance and accuracy compared to other methods. Thus, in this paper we propose to use Convolutional Neural Network (CNN) in the classification of Emphysema using a group of dataset provided. This report includes the background of emphysema and problems related to our objectives. Second chapter will discuss the previous study done by other researches related to CNN and lung diseases. Methodology and results obtained are shared in chapter three and four respectively. Some of the recommendation and references are listed at the end of this report.
format Final Year Project
author IBRAHIM, NURUL AIDA RADHIAH
spellingShingle IBRAHIM, NURUL AIDA RADHIAH
CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK
author_sort IBRAHIM, NURUL AIDA RADHIAH
title CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK
title_short CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK
title_full CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK
title_fullStr CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK
title_full_unstemmed CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORK
title_sort classification of emphysema using convolutional neural network
publishDate 2017
url http://utpedia.utp.edu.my/19087/1/%5BFYP%202%5D%20Dissertation%20Report.pdf
http://utpedia.utp.edu.my/19087/
_version_ 1741195447662280704
score 11.62408

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