Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Библиографические подробности
Главные авторы:	Hossain, Shah Md. Shakhawath, Alam, F M Tahoshin, Faiyaz, Hazra Mohammed Ahnaf
Другие авторы:	Alam, Md Ashraful
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2024
Предметы:	Convolutional neural networks Neuro-oncology Magnetic resonance imaging Ensemble models Guidance model Deep learning (Machine learning). Brain > Tumors.
Online-ссылка:	http://hdl.handle.net/10361/24008

id	10361-24008
record_format	dspace
spelling	10361-240082024-09-10T06:37:05Z Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance Hossain, Shah Md. Shakhawath Alam, F M Tahoshin Faiyaz, Hazra Mohammed Ahnaf Alam, Md Ashraful Department of Computer Science and Engineering, Brac University Convolutional neural networks Neuro-oncology Magnetic resonance imaging Ensemble models Guidance model Deep learning (Machine learning). Brain--Tumors. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-43). The early and accurate diagnosis of brain tumors is a critical challenge in medi cal imaging, significantly impacting treatment outcomes and patient survival rates. Despite the advancements in imaging technologies, the interpretation of MRI scans remains a complex and subjective task. This research introduces a novel cross modality deep learning approach aimed at enhancing the performance of multiclass brain tumor classification by leveraging superior imaging representations to guide and improve the analysis of less effective modalities. Our methodology involves the development of a guidance model that utilizes the robust representations de rived from high-quality imaging modalities to enhance the diagnostic accuracy of more practical but less efficient modalities. Specifically, we employed deep learn ing techniques to process and analyze MRI and histology data, including Convolu tional Neural Networks (CNNs) such as ResNet50, EfficientNetB0, InceptionV3, and DenseNet121. The guidance model integrates these representations to construct an ensemble model that achieves superior performance. The results demonstrate that our guidance model significantly improves the diagnostic accuracy of the subordinate modality. In the case of brain tumor classification, the model not only surpasses the performance of models trained solely on the superior modality but also achieves com parable results to those utilizing both modalities during inference with the guidance ensemble accuracy of 94.61%. Compared to this, other models such as Efficient NetB0 achieved 94% and DenseNet121 achieved 93% test accuracy. This approach offers a practical and efficient solution for enhancing diagnostic accuracy while mini mizing the reliance on more costly and less accessible imaging technologies. Overall, our cross-modality deep learning model represents a substantial advancement in the field of medical imaging, providing a more accurate, reliable, and cost-effective method for the diagnosis of brain tumors. Shah Md. Shakhawath Hossain F M Tahoshin Alam Hazra Mohammed Ahnaf Faiyaz B.Sc in Computer Science 2024-09-08T07:00:51Z 2024-09-08T07:00:51Z ©2024 2024-05 Thesis ID 18101133 ID 18101030 ID 17241014 http://hdl.handle.net/10361/24008 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 43 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Convolutional neural networks Neuro-oncology Magnetic resonance imaging Ensemble models Guidance model Deep learning (Machine learning). Brain--Tumors.
spellingShingle	Convolutional neural networks Neuro-oncology Magnetic resonance imaging Ensemble models Guidance model Deep learning (Machine learning). Brain--Tumors. Hossain, Shah Md. Shakhawath Alam, F M Tahoshin Faiyaz, Hazra Mohammed Ahnaf Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Alam, Md Ashraful
author_facet	Alam, Md Ashraful Hossain, Shah Md. Shakhawath Alam, F M Tahoshin Faiyaz, Hazra Mohammed Ahnaf
format	Thesis
author	Hossain, Shah Md. Shakhawath Alam, F M Tahoshin Faiyaz, Hazra Mohammed Ahnaf
author_sort	Hossain, Shah Md. Shakhawath
title	Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
title_short	Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
title_full	Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
title_fullStr	Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
title_full_unstemmed	Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
title_sort	enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24008
work_keys_str_mv	AT hossainshahmdshakhawath enhancingmulticlassbraintumorclassificationusingdeeplearningleveragingsuperiorimagingrepresentationstoimproveinferiormodalityperformance AT alamfmtahoshin enhancingmulticlassbraintumorclassificationusingdeeplearningleveragingsuperiorimagingrepresentationstoimproveinferiormodalityperformance AT faiyazhazramohammedahnaf enhancingmulticlassbraintumorclassificationusingdeeplearningleveragingsuperiorimagingrepresentationstoimproveinferiormodalityperformance
_version_	1814307447325589504

Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance

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