Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin

This project aims on the development of a Skin Burn Degree Detection system utilizing Convolutional Neural Network (CNN) algorithms. The background underscores the critical importance of early detection and treatment for skin burns to mitigate potential complications. Addressing the existing gap of...

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Main Author:	Salehuddin, Nur Amaleen Aishah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96390/1/96390.pdf
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id	my-uitm-ir.96390
record_format	uketd_dc
spelling	my-uitm-ir.963902024-06-05T03:58:12Z Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin 2024 Salehuddin, Nur Amaleen Aishah Neural networks (Computer science) This project aims on the development of a Skin Burn Degree Detection system utilizing Convolutional Neural Network (CNN) algorithms. The background underscores the critical importance of early detection and treatment for skin burns to mitigate potential complications. Addressing the existing gap of an accurate and efficient system for skin burn degree detection, the project aims to design and implement a CNN-based system with the capacity to precisely detect and classify different burn degrees. The methodology encompasses essential phases, including requirements gathering, design, implementation, testing, and evaluation. Through rigorous evaluation, the results demonstrate significant improvements in the system's overall performance over an extended training period of 300 epochs. Precision increases from 82.7% to 85.4%, and recall improves from 77.1% to 82.2%, indicating enhanced accuracy in classifying burn severity. The mean Average Precision (mAP) at IoU 0.5 exhibits notable advancement, rising from 79.7% to 83.5%, reflecting a more robust performance in object detection. Particularly, the mAP at IoU 0.5 to 0.95 experiences a substantial boost, ascending from 52.8% to 64.5%, indicating improved precision across a range of intersection-over-union thresholds. The conclusion underscores the significance of the proposed system, providing an accurate and efficient means for skin burn degree detection that holds immense potential for early diagnosis and treatment. This project contributes to the field of computer science by offering a practical application of the CNN algorithm within the healthcare industry. The improved performance metrics affirm the system's effectiveness and underscore its potential impact on advancing skin burn severity assessment. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96390/ https://ir.uitm.edu.my/id/eprint/96390/1/96390.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Noradzan, Haslinda
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Noradzan, Haslinda
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Salehuddin, Nur Amaleen Aishah Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin
description	This project aims on the development of a Skin Burn Degree Detection system utilizing Convolutional Neural Network (CNN) algorithms. The background underscores the critical importance of early detection and treatment for skin burns to mitigate potential complications. Addressing the existing gap of an accurate and efficient system for skin burn degree detection, the project aims to design and implement a CNN-based system with the capacity to precisely detect and classify different burn degrees. The methodology encompasses essential phases, including requirements gathering, design, implementation, testing, and evaluation. Through rigorous evaluation, the results demonstrate significant improvements in the system's overall performance over an extended training period of 300 epochs. Precision increases from 82.7% to 85.4%, and recall improves from 77.1% to 82.2%, indicating enhanced accuracy in classifying burn severity. The mean Average Precision (mAP) at IoU 0.5 exhibits notable advancement, rising from 79.7% to 83.5%, reflecting a more robust performance in object detection. Particularly, the mAP at IoU 0.5 to 0.95 experiences a substantial boost, ascending from 52.8% to 64.5%, indicating improved precision across a range of intersection-over-union thresholds. The conclusion underscores the significance of the proposed system, providing an accurate and efficient means for skin burn degree detection that holds immense potential for early diagnosis and treatment. This project contributes to the field of computer science by offering a practical application of the CNN algorithm within the healthcare industry. The improved performance metrics affirm the system's effectiveness and underscore its potential impact on advancing skin burn severity assessment.
format	Thesis
qualification_level	Bachelor degree
author	Salehuddin, Nur Amaleen Aishah
author_facet	Salehuddin, Nur Amaleen Aishah
author_sort	Salehuddin, Nur Amaleen Aishah
title	Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin
title_short	Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin
title_full	Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin
title_fullStr	Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin
title_full_unstemmed	Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin
title_sort	skin burn degree detection using convolutional neural network / nur amaleen aishah salehuddin
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96390/1/96390.pdf
_version_	1804889987754229760

Skin burn degree detection using Convolutional Neural Network / Nur Amaleen Aishah Salehuddin

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