Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging

Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging

In the realm of medical imaging, the precise segmentation and classification of gliomas represent fundamental challenges with profound clinical implications. Leveraging the BraTS 2018 dataset as a standard benchmark, this study delves into the potential of advanced deep learning models for addressin...

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Bibliographic Details
Main Authors: Sonam Saluja, Munesh Chandra Trivedi, Shiv S. Sarangdevot
Format: Article
Language:English
Published: AIMS Press 2024-02-01
Series:Mathematical Biosciences and Engineering
Subjects:
gliomas
deep learning
segmentation
classification
u-net
vgg-16
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2024191?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2024191?viewType=HTML

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