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Advances in Brain Tumor Diagnosis Through Multimodal Imaging and Deep Learning Techniques
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Author(s): Liyakathunisa Syed (Taibah University, Saudi Arabia), Roba Alhashmiy Alamir (Taibah University, Saudi Arabia), Ghada Alharbi (Taibah University, Saudi Arabia), Abdullah Alsaeedi (Taibah University, Saudi Arabia), Ayman Noor (Taibah University, Saudi Arabia)and Talal Noor (Taibah University, Saudi Arabia)
Copyright: 2026
Pages: 34
Source title:
AI and Machine Learning for Cancer Care: Precision Medicine and Beyond
Source Author(s)/Editor(s): Manvi Mishra (Shri Ram Murti Smarak College of Engineering and Technology, Bareilly, India), Piyush Kumar (Shri Ram Murti Smarak Institute of Medical Sciences, Bareilly, India), Himanshi Khattar (Shri Ram Murti Smarak Institute of Medical Sciences, Bareilly, India)and Mohammad Zubair Khan (Islamic University of Madinah, Saudi Arabia)
DOI: 10.4018/979-8-3373-4312-9.ch005
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Abstract
Brain tumors represent abnormal growths of cells—either benign or malignant—within the confined space of the human skull. These growths can lead to severe health complications and long-term psychological and physical consequences, greatly diminishing a patient's quality of life. The survival rate for individuals diagnosed with malignant brain tumors decreases significantly over time if early interventions are not implemented. This study investigates advanced hybrid architectures, namely CNN-UNet, ResNet-UNet, EfficientNet-UNet, and EfficientNet integrated with Vision Transformers. A comprehensive comparative analysis of these hybrid architectures is conducted to recognize the most efficient model. The results indicate that EfficientNet-UNet architecture achieved superior performance on the BraTS dataset with an accuracy of 99.47%, while EfficientNet-Vision Transformer on the Nickparvar dataset from Kaggle reached an accuracy of 97%. These findings highlight the efficiency of advanced hybrid deep learning models in detecting brain tumors from multimodal MRI images.
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