Development of lung cancer prediction system using meta-heuristic optimized deep learning model

Lung cancer is a serious disease that completely affects the human respiratory system for both men and women. The lung cancer symptoms create complexity in detecting lung cancer in the earlier stage. For this purpose, an automatic computer-aided lung cancer detection system is required to minimize t...

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Main Author:	Mohamed Shakeel, Pethuraj
Format:	Thesis
Language:	English English
Published:	2023
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/28305/1/Development%20of%20lung%20cancer%20prediction%20system%20using%20meta-heuristic%20optimized%20deep%20learning%20model.pdf http://eprints.utem.edu.my/id/eprint/28305/2/Development%20of%20lung%20cancer%20prediction%20system%20using%20meta-heuristic%20optimized%20deep%20learning%20model.pdf
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spelling	my-utem-ep.283052024-12-16T08:30:03Z Development of lung cancer prediction system using meta-heuristic optimized deep learning model 2023 Mohamed Shakeel, Pethuraj T Technology (General) TA Engineering (General). Civil engineering (General) Lung cancer is a serious disease that completely affects the human respiratory system for both men and women. The lung cancer symptoms create complexity in detecting lung cancer in the earlier stage. For this purpose, an automatic computer-aided lung cancer detection system is required to minimize the mortality rate by recognizing it in an earlier stage. The traditional system fails to predict the accurate affected cancer region with minimum computation complexity and error rate. For overcoming the difficulties, effective and optimized meta-heuristic machine learning techniques indicate lung cancer in the earlier stage. The proposed optimized automatic lung cancer prediction process minimizes the entire miss-classification and improves lung cancer recognition accuracy. The machine learning techniques are used to predict lung cancer in this study. First, the Computed Tomography (CT) images obtained from the Cancer Imaging Archive (CIA) dataset processed with the help of a weighted mean histogram equalization approach that used to eliminate the noise information from CT image. After that cancer-affected region in the lung is segmented with the help of the proposed Butterfly Optimization Algorithm-based K-Means Clustering (BOAKMC) algorithm. The algorithm detects the affected region depending on pixel similarity computation process. Then different features are derived from the segmented region using Gray Intensity Co-Occurrence Distribution Matrix (GICDM) which is processed by applying a proposed Supervised Jaya Optimized Rough Set based Feature Selection (SJORSFS) algorithm. These algorithms select the best features according to the fitness value, and its redundancy is to be reduced. Finally, the classification is implemented using an ensemble classifier, deep learning instantaneously trained a neural network and an Autoencoder-based Recurrent Neural Network (ARNN) classification algorithm. The proposed lung cancer prediction model recognizes the lung cancer up to 96.39% of accuracy, 0.981% of precision value, 0.9839% of F1-score, 6.438% of false positive rate and 448.607ms of classification time. 2023 Thesis http://eprints.utem.edu.my/id/eprint/28305/ http://eprints.utem.edu.my/id/eprint/28305/1/Development%20of%20lung%20cancer%20prediction%20system%20using%20meta-heuristic%20optimized%20deep%20learning%20model.pdf text en public http://eprints.utem.edu.my/id/eprint/28305/2/Development%20of%20lung%20cancer%20prediction%20system%20using%20meta-heuristic%20optimized%20deep%20learning%20model.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=123820 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Mohd. Aboobaider, Burhanuddin
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Mohd. Aboobaider, Burhanuddin
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Mohamed Shakeel, Pethuraj Development of lung cancer prediction system using meta-heuristic optimized deep learning model
description	Lung cancer is a serious disease that completely affects the human respiratory system for both men and women. The lung cancer symptoms create complexity in detecting lung cancer in the earlier stage. For this purpose, an automatic computer-aided lung cancer detection system is required to minimize the mortality rate by recognizing it in an earlier stage. The traditional system fails to predict the accurate affected cancer region with minimum computation complexity and error rate. For overcoming the difficulties, effective and optimized meta-heuristic machine learning techniques indicate lung cancer in the earlier stage. The proposed optimized automatic lung cancer prediction process minimizes the entire miss-classification and improves lung cancer recognition accuracy. The machine learning techniques are used to predict lung cancer in this study. First, the Computed Tomography (CT) images obtained from the Cancer Imaging Archive (CIA) dataset processed with the help of a weighted mean histogram equalization approach that used to eliminate the noise information from CT image. After that cancer-affected region in the lung is segmented with the help of the proposed Butterfly Optimization Algorithm-based K-Means Clustering (BOAKMC) algorithm. The algorithm detects the affected region depending on pixel similarity computation process. Then different features are derived from the segmented region using Gray Intensity Co-Occurrence Distribution Matrix (GICDM) which is processed by applying a proposed Supervised Jaya Optimized Rough Set based Feature Selection (SJORSFS) algorithm. These algorithms select the best features according to the fitness value, and its redundancy is to be reduced. Finally, the classification is implemented using an ensemble classifier, deep learning instantaneously trained a neural network and an Autoencoder-based Recurrent Neural Network (ARNN) classification algorithm. The proposed lung cancer prediction model recognizes the lung cancer up to 96.39% of accuracy, 0.981% of precision value, 0.9839% of F1-score, 6.438% of false positive rate and 448.607ms of classification time.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohamed Shakeel, Pethuraj
author_facet	Mohamed Shakeel, Pethuraj
author_sort	Mohamed Shakeel, Pethuraj
title	Development of lung cancer prediction system using meta-heuristic optimized deep learning model
title_short	Development of lung cancer prediction system using meta-heuristic optimized deep learning model
title_full	Development of lung cancer prediction system using meta-heuristic optimized deep learning model
title_fullStr	Development of lung cancer prediction system using meta-heuristic optimized deep learning model
title_full_unstemmed	Development of lung cancer prediction system using meta-heuristic optimized deep learning model
title_sort	development of lung cancer prediction system using meta-heuristic optimized deep learning model
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2023
url	http://eprints.utem.edu.my/id/eprint/28305/1/Development%20of%20lung%20cancer%20prediction%20system%20using%20meta-heuristic%20optimized%20deep%20learning%20model.pdf http://eprints.utem.edu.my/id/eprint/28305/2/Development%20of%20lung%20cancer%20prediction%20system%20using%20meta-heuristic%20optimized%20deep%20learning%20model.pdf
_version_	1818612069888425984

Development of lung cancer prediction system using meta-heuristic optimized deep learning model

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