Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging
In: Mathematical Biosciences and Engineering, Jg. 21 (2024), Heft 3, S. 4328-4350
Online
academicJournal
Zugriff:
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 addressing these challenges. We propose a novel approach that integrates a customized U-Net for segmentation and VGG-16 for classification. The U-Net, with its tailored encoder-decoder pathways, accurately identifies glioma regions, thus improving tumor localization. The fine-tuned VGG-16, featuring a customized output layer, precisely differentiates between low-grade and high-grade gliomas. To ensure consistency in data pre-processing, a standardized methodology involving gamma correction, data augmentation, and normalization is introduced. This novel integration surpasses existing methods, offering significantly improved glioma diagnosis, validated by high segmentation dice scores (WT: 0.96, TC: 0.92, ET: 0.89), and a remarkable overall classification accuracy of 97.89%. The experimental findings underscore the potential of integrating deep learning-based methodologies for tumor segmentation and classification in enhancing glioma diagnosis and formulating subsequent treatment strategies.
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Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging
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Autor/in / Beteiligte Person: | Saluja, Sonam ; Munesh Chandra Trivedi ; Sarangdevot, Shiv S. |
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Zeitschrift: | Mathematical Biosciences and Engineering, Jg. 21 (2024), Heft 3, S. 4328-4350 |
Veröffentlichung: | AIMS Press, 2024 |
Medientyp: | academicJournal |
ISSN: | 1551-0018 (print) |
DOI: | 10.3934/mbe.2024191?viewType=HTML |
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