Brain Tumor Detection and Classification

NeCS-Flow: Necrosis-Guided Contrast-Enhanced MRI Synthesis with Rectified-Flow Refinement

NeCS-Flow synthesizes gadolinium-free post-contrast T1w brain MRI using necrotic scaling and PMRF refinement, achieving perceptual realism and diagnostic equivalence.

Slice, Subtract, Segment: Efficient 2D and Physiologically Informed 3D Brain Tumor Segmentation

This paper evaluates two approaches for improving brain tumor segmentation: a novel 2D U-Net architecture and a multi-channel input representation using cross-modal subtraction maps, showing both contribute to performance improvements.

NeuroMesh: A Bottleneck Topology Controller for Missing-Modality Brain Tumor Segmentation - A Mechanistic Pilot Study on BraTS

NeuroMesh, a bottleneck controller for brain tumor segmentation, shows improved performance in missing-modality conditions but fails in FLAIR absence, highlighting a discrepancy between design intent and actual behavior.

DCGAN-Based Synthetic MRI Augmentation for Data-centric Brain Tumor Segmentation

This paper uses DCGAN to generate synthetic MRI images for brain tumor segmentation, improving Dice coefficient and IoU metrics compared to a baseline model.

A New Hybrid Method for Brain Tumor Detection Based on Deep Learning

This paper introduces a hybrid DCNN-WOA framework for brain tumor detection and classification, achieving high accuracy and outperforming existing methods on an MRI dataset.

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

PRCnet is a model for automatic brain tumor detection in MRI images, achieving high accuracy on two datasets.

Diet-Seg: Dynamic Hardness-Aware Learning for Enhanced Brain Tumor Segmentation

Diet-Seg improves brain tumor segmentation in MRI by using entropy-based hardness estimation and dynamic learning rates, outperforming existing methods across multiple datasets.

XAI-MRI: An Ensemble Dual-Modality Approach for 3D Brain Tumor Segmentation Using Magnetic Resonance Imaging

This paper proposes an ensemble dual-modality approach for 3D brain tumor segmentation using MRI, achieving high Dice Coefficient and Mean IoU on BraTS2020.

Deep Learning and Transfer Learning for Brain Tumor Detection and Classification

This study uses transfer learning with CNNs trained on brain tumor MRI data and camouflage animal detection to improve tumor classification accuracy and model generalization.

Analysis of Lower-Grade Gliomas in MRI Through Segmentation and Genomic Cluster-Shape Feature Correlation

This paper uses deep learning and statistical analysis to correlate shape features of lower-grade gliomas with genomic subtypes in MRI data.

MRI-BASED DEEP LEARNING METHOD FOR DETERMINING METHYLATION STATUS OF THE O6 METHYLGUANINE DNA METHYLTRANSFERASE PROMOTER OUTPERFORMS TISSUE BASED METHODS IN BRAIN GLIOMAS

This paper develops a deep learning model using T2-w MRI to predict MGMT promoter methylation in gliomas, achieving higher accuracy than histological methods.

A Novel Fully Automated MRI-Based Deep Learning Method For Classification Of 1p/19q Co-Deletion Status In Brain Gliomas

This paper develops a deep learning model using T2-weighted MRI to classify 1p/19q co-deletion status in brain gliomas with high accuracy.

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

This paper develops a deep learning method for automated brain tumor segmentation and survival prediction using MRI data from the BraTS2019 dataset, achieving high segmentation accuracy and moderate survival prediction performance.

A Novel Fully Automated MRI-Based Deep Learning Method For Classification Of IDH Mutation Status In Brain Gliomas

The paper withdraws its manuscript due to errors in analysis and requests it not be cited.

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

The paper develops and ensembles 2D/3D segmentation models for brain tumors using multiresolution inputs, achieving high DICE scores and survival prediction accuracy on BRATS 2019 data.

Classification of Brain Tumor IDH Status using MRI and Deep Learning

This paper develops a deep learning model to predict IDH mutation status from T2-weighted MRI scans of brain tumors, achieving 83.8% test accuracy.