Brain Tumor Detection and Classification

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

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

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.

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

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

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.

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