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Integrated Deep Learning Approach for Brain Tumor Detection and Segmentation in MRI Images |
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PP: 27-36 |
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doi:10.18576/aeta/130103
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Author(s) |
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Bassam A Jaradat,
Khaled Bawaneh,
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Abstract |
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| This study examines the use of deep learning techniques for magnetic resonance imaging (MRI)-based brain
tumor categorization in lower-grade gliomas by applying the manually segmented FLAIR abnormality masks that
were acquired from The Cancer Imaging Archive (TCIA) and the LGG Segmentation Dataset, which include brain
MR images. We propose a classifier model based on ResNet50 and a segmentation model named U-NET, leveraging
knowledge from earlier research investigating the relationship between form data collected by deep learning
algorithms and genetic subtypes of lower-grade gliomas. This model reaches a remarkable accuracy of 94.75% when
trained to identify whether tumors are present or absent. F1-score, precision, and recall measures are included in the
evaluation to give a thorough understanding of the models functionality. These findings highlight the promise of
sophisticated image processing methods for precise and automated brain tumor classification, with ramifications for
improving neuro-oncology clinical processes and diagnostic accuracy.
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