Automated brain tumor detection with artificial intelligence: a machine learning approach

This Final Master Project, "Automated Brain Tumor Detection with Artificial Intelligence: A Machine Learning Approach," focused on designing, implementing, and evaluating machine learning models for the automated detection and classification of brain tumours using MRI data. The cor...

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Detalhes bibliográficos
Autor: Woloszyn, Michal
Formato: tesis de maestría
Fecha de publicación:2025
País:España
Recursos:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/153553
Acesso em linha:https://hdl.handle.net/10609/153553
Access Level:acceso abierto
Palavra-chave:machine learning
image classification
Machine learning -- TFM
Aprenentatge automàtic -- TFM
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repository_id_str
spelling Automated brain tumor detection with artificial intelligence: a machine learning approachWoloszyn, Michalmachine learningimage classificationMachine learning -- TFMAprenentatge automàtic -- TFMThis Final Master Project, "Automated Brain Tumor Detection with Artificial Intelligence: A Machine Learning Approach," focused on designing, implementing, and evaluating machine learning models for the automated detection and classification of brain tumours using MRI data. The core purpose was to enhance diagnostic accuracy, reduce human error, and improve patient outcomes. The implementation involved an iterative approach, starting with the development of several custom Convolutional Neural Networks (CNN1-4). These initial models progressively refined their architecture, introduced dynamic class weighting to address imbalance, and utilised data augmentation to establish a robust baseline. Building on these foundational efforts, the project transitioned to transfer learning models, beginning with ResNet50 and its enhanced variant (ResNet50 2), leveraging pre-trained weights for more sophisticated feature extraction. The project culminated in the Xception family of models (Xception 1-4), which demonstrated superior performance through successive advancements.Universitat Oberta de Catalunya (UOC)Sarasa Cabezuelo, Antonio202520252025info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttps://hdl.handle.net/10609/153553reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésCC BY-NC-NDhttp://creativecommons.org/licenses/by-nc-nd/4.0/es/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1535532026-05-28T12:42:01Z
dc.title.none.fl_str_mv Automated brain tumor detection with artificial intelligence: a machine learning approach
title Automated brain tumor detection with artificial intelligence: a machine learning approach
spellingShingle Automated brain tumor detection with artificial intelligence: a machine learning approach
Woloszyn, Michal
machine learning
image classification
Machine learning -- TFM
Aprenentatge automàtic -- TFM
title_short Automated brain tumor detection with artificial intelligence: a machine learning approach
title_full Automated brain tumor detection with artificial intelligence: a machine learning approach
title_fullStr Automated brain tumor detection with artificial intelligence: a machine learning approach
title_full_unstemmed Automated brain tumor detection with artificial intelligence: a machine learning approach
title_sort Automated brain tumor detection with artificial intelligence: a machine learning approach
dc.creator.none.fl_str_mv Woloszyn, Michal
author Woloszyn, Michal
author_facet Woloszyn, Michal
author_role author
dc.contributor.none.fl_str_mv Sarasa Cabezuelo, Antonio
dc.subject.none.fl_str_mv machine learning
image classification
Machine learning -- TFM
Aprenentatge automàtic -- TFM
topic machine learning
image classification
Machine learning -- TFM
Aprenentatge automàtic -- TFM
description This Final Master Project, "Automated Brain Tumor Detection with Artificial Intelligence: A Machine Learning Approach," focused on designing, implementing, and evaluating machine learning models for the automated detection and classification of brain tumours using MRI data. The core purpose was to enhance diagnostic accuracy, reduce human error, and improve patient outcomes. The implementation involved an iterative approach, starting with the development of several custom Convolutional Neural Networks (CNN1-4). These initial models progressively refined their architecture, introduced dynamic class weighting to address imbalance, and utilised data augmentation to establish a robust baseline. Building on these foundational efforts, the project transitioned to transfer learning models, beginning with ResNet50 and its enhanced variant (ResNet50 2), leveraging pre-trained weights for more sophisticated feature extraction. The project culminated in the Xception family of models (Xception 1-4), which demonstrated superior performance through successive advancements.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/10609/153553
url https://hdl.handle.net/10609/153553
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv CC BY-NC-ND
http://creativecommons.org/licenses/by-nc-nd/4.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY-NC-ND
http://creativecommons.org/licenses/by-nc-nd/4.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869408125409296384
score 15,812429

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