Cover Image

DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING

Brain cancer is a serious medical condition that requires an accurate and timely diagnosis for effective treatment planning. In recent years, deep learning techniques have shown great potential in the field of medical image analysis. In this study, a brain cancer detection and classification system...

Full description

Saved in:
Bibliographic Details
Main Author: Asvin Kumar, A/L Moghan
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/43144/1/Asvin%20Kumar%20Moghan%2024pgs.pdf
http://ir.unimas.my/id/eprint/43144/7/Asvin%20Kumar%20%20ft.pdf
http://ir.unimas.my/id/eprint/43144/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimas.ir-43144
record_format eprints
spelling my.unimas.ir-431442024-12-17T06:15:26Z http://ir.unimas.my/id/eprint/43144/ DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING Asvin Kumar, A/L Moghan T Technology (General) Brain cancer is a serious medical condition that requires an accurate and timely diagnosis for effective treatment planning. In recent years, deep learning techniques have shown great potential in the field of medical image analysis. In this study, a brain cancer detection and classification system based on deep learning algorithms is proposed. The system utilises a convolutional neural network (CNN) architecture trained on a large dataset of brain MRI images. The images were preprocessed to enhance relevant features and remove noise. The CNN architecture chosen was GoogleNet. To validate the robustness of the system, a 5-fold cross-validation approach was employed, ensuring reliable and consistent results. The proposed system has the potential to assist medical professionals in the early detection and classification of brain tumours, aiding in accurate diagnosis and treatment decision-making. By automating the classification process, it reduces the burden of manual analysis, potentially saving time and improving the overall efficiency of the diagnostic process. The proposed model achieved an accuracy of 97.5522±0.2739%, a precision of 0.9498±0.0054, a recall of 0.9494±0.0057, a specificity 0.9839±0.0018 and an F1 Score of 0.9493±0.0057 across the 5-fold cross-validation iterations, demonstrating its effectiveness in accurately classifying brain MRI. Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/43144/1/Asvin%20Kumar%20Moghan%2024pgs.pdf text en http://ir.unimas.my/id/eprint/43144/7/Asvin%20Kumar%20%20ft.pdf Asvin Kumar, A/L Moghan (2023) DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Asvin Kumar, A/L Moghan
DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING
description Brain cancer is a serious medical condition that requires an accurate and timely diagnosis for effective treatment planning. In recent years, deep learning techniques have shown great potential in the field of medical image analysis. In this study, a brain cancer detection and classification system based on deep learning algorithms is proposed. The system utilises a convolutional neural network (CNN) architecture trained on a large dataset of brain MRI images. The images were preprocessed to enhance relevant features and remove noise. The CNN architecture chosen was GoogleNet. To validate the robustness of the system, a 5-fold cross-validation approach was employed, ensuring reliable and consistent results. The proposed system has the potential to assist medical professionals in the early detection and classification of brain tumours, aiding in accurate diagnosis and treatment decision-making. By automating the classification process, it reduces the burden of manual analysis, potentially saving time and improving the overall efficiency of the diagnostic process. The proposed model achieved an accuracy of 97.5522±0.2739%, a precision of 0.9498±0.0054, a recall of 0.9494±0.0057, a specificity 0.9839±0.0018 and an F1 Score of 0.9493±0.0057 across the 5-fold cross-validation iterations, demonstrating its effectiveness in accurately classifying brain MRI.
format Final Year Project Report
author Asvin Kumar, A/L Moghan
author_facet Asvin Kumar, A/L Moghan
author_sort Asvin Kumar, A/L Moghan
title DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING
title_short DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING
title_full DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING
title_fullStr DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING
title_full_unstemmed DETECTION AND CLASSIFICATION OF BRAIN CANCER USING DEEP LEARNING
title_sort detection and classification of brain cancer using deep learning
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/43144/1/Asvin%20Kumar%20Moghan%2024pgs.pdf
http://ir.unimas.my/id/eprint/43144/7/Asvin%20Kumar%20%20ft.pdf
http://ir.unimas.my/id/eprint/43144/
_version_ 1818839380241940480
score 13.222552

Similar Items