Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification

Skin cancer is one of the most lethal illnesses in humans. Dermatologists spend much more time investigating these lesions due to the high similarities between different skin cancer forms. The existing methods, such as ABCDE rule and 7-point checklist, can guide the dermatologists to analyse the ski...

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Main Author:	KA CHIN, CHEE
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/38302/3/Chee.pdf
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id	my-unimas-ir.38302
record_format	uketd_dc
spelling	my-unimas-ir.383022023-11-10T02:58:08Z Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification 2022-04-13 KA CHIN, CHEE QA75 Electronic computers. Computer science T Technology (General) Skin cancer is one of the most lethal illnesses in humans. Dermatologists spend much more time investigating these lesions due to the high similarities between different skin cancer forms. The existing methods, such as ABCDE rule and 7-point checklist, can guide the dermatologists to analyse the skin lesion, but these techniques can only distinguish benign and malignant of skin cancer. The use of Artificial Intelligence (AI) based Computer-Aided Decision (CAD) systems on automating the classification of skin cancer would save time, effort, and human lives. Although machine learning and deep learning are widely used in skin cancer diagnosis, machine learning is still unable to get the deep features from network flow. Deep learning also has the complex network with an enormous number of parameters that may resulting in the low and limited classification accuracy performance. In this research, the process of AI-based CAD system for skin cancer classification is introduced. The suitable image pre-processing technique and image augmentation method are identified to avoid the degradation of classification accuracy in this research. Other than that, this research also focuses on the use of Convolutional Neural Network (CNN) as a feature extractor and Auto-Regressive Integrated Moving Average (ARIMA) behaved as a skin cancer classifier. Thus, the new hybrid CNN-ARIMA is proposed, which is able to classify skin cancer images successfully with test accuracy, average sensitivity, average specificity, average precision, and AUC of 96.00%, 96.02%, 97.98%, 96.13%, and 0.995, respectively. Universiti Malaysia Sarawak 2022-04 Thesis http://ir.unimas.my/id/eprint/38302/ http://ir.unimas.my/id/eprint/38302/3/Chee.pdf text en validuser masters Universiti Malaysia Sarawak Department of Electrical and Electronics
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science T Technology (General)
spellingShingle	QA75 Electronic computers Computer science T Technology (General) KA CHIN, CHEE Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification
description	Skin cancer is one of the most lethal illnesses in humans. Dermatologists spend much more time investigating these lesions due to the high similarities between different skin cancer forms. The existing methods, such as ABCDE rule and 7-point checklist, can guide the dermatologists to analyse the skin lesion, but these techniques can only distinguish benign and malignant of skin cancer. The use of Artificial Intelligence (AI) based Computer-Aided Decision (CAD) systems on automating the classification of skin cancer would save time, effort, and human lives. Although machine learning and deep learning are widely used in skin cancer diagnosis, machine learning is still unable to get the deep features from network flow. Deep learning also has the complex network with an enormous number of parameters that may resulting in the low and limited classification accuracy performance. In this research, the process of AI-based CAD system for skin cancer classification is introduced. The suitable image pre-processing technique and image augmentation method are identified to avoid the degradation of classification accuracy in this research. Other than that, this research also focuses on the use of Convolutional Neural Network (CNN) as a feature extractor and Auto-Regressive Integrated Moving Average (ARIMA) behaved as a skin cancer classifier. Thus, the new hybrid CNN-ARIMA is proposed, which is able to classify skin cancer images successfully with test accuracy, average sensitivity, average specificity, average precision, and AUC of 96.00%, 96.02%, 97.98%, 96.13%, and 0.995, respectively.
format	Thesis
qualification_level	Master's degree
author	KA CHIN, CHEE
author_facet	KA CHIN, CHEE
author_sort	KA CHIN, CHEE
title	Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification
title_short	Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification
title_full	Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification
title_fullStr	Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification
title_full_unstemmed	Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification
title_sort	development of hybrid convolutional neural network and auto-regressive integrated moving average for skin cancer classification
granting_institution	Universiti Malaysia Sarawak
granting_department	Department of Electrical and Electronics
publishDate	2022
url	http://ir.unimas.my/id/eprint/38302/3/Chee.pdf
_version_	1783728498458755072

Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification

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