Jul 25, 2024 · In this paper, we propose a simple mean image transformation applied to all four sequences to reduce data size and alleviate the demand for extensive data ...
Convolutional neural networks are able to learn discriminating features automatically, and these features provide added value for grading gliomas.
In this work, we explore the potential of training CNN by combining these four sequences to form a mean image that encapsulates core characteristics of tumor.
Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning.
A multimodal imaging approach with DCE and DKI improves diagnostic confidence and yields higher diagnostic accuracy for predicting tumor grade and type in adult ...
Missing: Transformation. | Show results with:Transformation.
Results: The results indicate the ML models trained with learned features had an improvement of at least 19% in terms of F1-score compared to radiomic features.
Missing: Transformation. | Show results with:Transformation.
May 25, 2024 · We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method.
People also ask
What is a malignant transformation of a glioma?
What are the grades of gliomas in MRI?
Dec 19, 2019 · We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1 ...
Missing: Improved | Show results with:Improved
ABSTRACT Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making. We present a novel wavelet scattering-based ...
This study aims to address this issue through MRI-based classification to develop an accurate model for glioma diagnosis.