Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami

Monkeypox is a rare disease caused by the monkeypox virus and is classified as a poxviridae and orthopoxviral virus. It is an important health issue because of the possibility that it will spread quickly and share similarities to other diseases like measles and chickenpox. Despite the name “monkeypo...

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Main Author:	Ghanami, Nur Farhain Humaira
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96441/1/96441.pdf
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id	my-uitm-ir.96441
record_format	uketd_dc
spelling	my-uitm-ir.964412024-06-05T23:35:27Z Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami 2024 Ghanami, Nur Farhain Humaira Neural networks (Computer science) Monkeypox is a rare disease caused by the monkeypox virus and is classified as a poxviridae and orthopoxviral virus. It is an important health issue because of the possibility that it will spread quickly and share similarities to other diseases like measles and chickenpox. Despite the name “monkeypox”, the disease comes from mice and rats. Detecting monkeypox disease early is challenging due to symptoms like chickenpox and measles, limited skin lesion images, and lack of training examples, requiring CNN integration. Thus, this research project aims to develop a prototype for detecting Monkeypox Disease Detection using Convolutional Neural Network (CNN) and detect monkeypox disease, which can assist in reducing its spread and improve patient outcomes. The project is to study the CNN algorithm and develop a prototype to evaluate the accuracy of monkeypox disease detection using CNN. CNN's twodimensional internal representation enhances determining shape and size in data structures, particularly with images. CNN performance depends on the quantity and quality of pre-processed datasets for standardized outcomes. The study achieved 93.33% accuracy in monkeypox detection using the CNN algorithm. However, there are some limitations which be limited due to a small dataset. Overfitting and class imbalance are possible problems that need a detailed examination of model complexity and training methods. In conclusion, the prototype's performance supports the project's potential for advances in disease detection technologies and improved patient outcomes, leading the path for more widespread healthcare diagnostics. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96441/ https://ir.uitm.edu.my/id/eprint/96441/1/96441.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Ahmad, Jasmin Ilyani
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ahmad, Jasmin Ilyani
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Ghanami, Nur Farhain Humaira Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami
description	Monkeypox is a rare disease caused by the monkeypox virus and is classified as a poxviridae and orthopoxviral virus. It is an important health issue because of the possibility that it will spread quickly and share similarities to other diseases like measles and chickenpox. Despite the name “monkeypox”, the disease comes from mice and rats. Detecting monkeypox disease early is challenging due to symptoms like chickenpox and measles, limited skin lesion images, and lack of training examples, requiring CNN integration. Thus, this research project aims to develop a prototype for detecting Monkeypox Disease Detection using Convolutional Neural Network (CNN) and detect monkeypox disease, which can assist in reducing its spread and improve patient outcomes. The project is to study the CNN algorithm and develop a prototype to evaluate the accuracy of monkeypox disease detection using CNN. CNN's twodimensional internal representation enhances determining shape and size in data structures, particularly with images. CNN performance depends on the quantity and quality of pre-processed datasets for standardized outcomes. The study achieved 93.33% accuracy in monkeypox detection using the CNN algorithm. However, there are some limitations which be limited due to a small dataset. Overfitting and class imbalance are possible problems that need a detailed examination of model complexity and training methods. In conclusion, the prototype's performance supports the project's potential for advances in disease detection technologies and improved patient outcomes, leading the path for more widespread healthcare diagnostics.
format	Thesis
qualification_level	Bachelor degree
author	Ghanami, Nur Farhain Humaira
author_facet	Ghanami, Nur Farhain Humaira
author_sort	Ghanami, Nur Farhain Humaira
title	Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami
title_short	Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami
title_full	Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami
title_fullStr	Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami
title_full_unstemmed	Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami
title_sort	monkeypox disease detection using convolutional neural network (cnn) / nur farhain humaira ghanami
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96441/1/96441.pdf
_version_	1804889988501864448

Monkeypox disease detection using Convolutional Neural Network (CNN) / Nur Farhain Humaira Ghanami

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