| dc.description.abstract | Early detection and classification of brain tumor grade are crucial for improving treatment and
potentially prolonging survival rate. This work aims to address this issue using MRI-based
classification and to create an accurate model for the diagnosis of gliomas. This research presents
a deep learning pipeline consisting of three main steps: (1) MRI brain tumor images are segmented
using preprocessing methods with two custom UNet architecture; (2) Collect regions of brain
tumor are extracted bases on the prediction of segmentation; and first approach (3) HGG and LGG
are classified through the implementation of a custom CNN model architecture; second approach
(4) Grade 1, grade 2, grade 3 are classified by using pre-trained VGG-19. The proposed network
structure achieves a significant performance, with the best overall accuracy of 98.9 % and 85.14
%, respectively, for 2 classes and 3 classes. This model can potentially be integrated into a
computer-aided diagnosis system that supports clinicians in the diagnosis of gliomas. | en_US |
