A Deep Learning-Driven Brain Tumour Segmentation using a Hybrid U-Net and LSTM Architecture

BRAIN TUMOR SEGMENTATION OF MRI SEQUENCES (T1, T2, T1CE, FLAIR) USING BRATS DATASET

Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is an important task in computer-aided diagnosis because accurate identification of tumor regions supports clinical assessment and treatment planning. However, the complex structure, irregular shape, intensity variation, and heterogeneous appearance of brain tumors make automated segmentation challenging. This study presents a comparative deep learning framework for brain tumor segmentation using the BraTS 2020 dataset and two-dimensional (2D) MRI images. In this study, we explore 2D deep learning architectures for automated tumor segmentation, focusing on U-Net, Vision Transformer (ViT), and Res-ViT models. U-Net, with its encoder–decoder design and skip connections, has been widely adopted for medical image segmentation due to its ability to capture fine-grained spatial features. ViT, leveraging self-attention mechanisms, introduces a global receptive field that enhances contextual understanding across slices. The Res-ViT hybrid combines residual learning with transformer-based attention, aiming to balance local feature extraction and long-range dependency modelling. Preprocessing steps, including skull stripping, intensity normalisation, and bias field correction, were applied to ensure consistency across scans. Data augmentation techniques such as rotation, flipping, and elastic deformation were employed to mitigate overfitting and improve generalisation. The models are evaluated using important segmentation metrics, including Intersection over Union (IoU), accuracy, precision, recall/sensitivity, loss, and 95th-percentile Hausdorff Distance (HD95). The comparative analysis aims to identify the strengths and limitations of convolutional and transformer-based approaches for 2D brain tumor segmentation. The study demonstrates the potential of combining local feature extraction and global contextual learning to achieve more accurate and robust brain tumor segmentation from multimodal MRI images. Keywords: MRI; U-Net; ViT; BraTS 2020; IOU; HD95.

RLU-Net-GD: a deep supervised residual U-Net for enhanced brain tumor segmentation

Multimodal Brain Tumour Classification and Segmentation Using a Dual-Attention Swin-UNet with Quantum-Inspired Optimisation for MRI-Based Diagnostics

A Multimodal Brain Tumour Classification and Segmentation framework based on a Dual-Attention Swin-UNet architecture, enhanced with a Quantum-Inspired Optimisation (QIO) technique, to overcome limitations in transformer-based medical imaging models.

Accurate Brain Tumor Classification Using MRI Images Based on A Hybrid Vision Transformer and BiLSTM Framework

Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater.

A Dual-Attention and Uncertainty-Aware Deep Learning Framework for Clinically Trusted Brain Tumor Segmentation

Although brain tumour segmentation is essential for medical image analysis, reliable and accurate segmentation with clinical and routine quality appears to be difficult because of the heterogeneity of the tumour, the lack of clear boundaries and the noise within the images. Current deep learning (DL) models have been plagued by the problem of the black box effect and overconfident predictions, limiting their use in real clinical settings. In fact, current DL models have experienced the issue of black box effect and high prediction accuracy, which prevent the models from being used in the real clinical setting. To solve these, the study puts forward a Dual-Attention and Uncertainty-Aware DL framework to enhance segmentation accuracy and clinical trustworthiness. The method combines both spatial and channel attention mechanism and is implemented in an encoder-decoder framework which allows obtaining more informative features from multi-modal MRI scans. Besides, a multi-scale feature fusion module is used to capture tumors of different scales, and an uncertainty-aware module is used to measure the confidence of prediction by computing the stochastic inference. A clinical trust calibration layer is added to make the predicted probabilities more realistic. Experiments performed on the BraTS dataset show better performance when compared with the state-of-the-art models with Dice score, IoU, sensitivity, and specificity of 94.8%, 89.5%, 93.6%, and 97.1%, respectively. The proposed framework not only enhances the segmentation accuracy but also generates the uncertainty maps, which are well suited for a clinical decision support system. Finally, the model provides a reliable, interpretable, and strong brain tumor segmentation solution, which has great potential in medical applications.

An Efficient and Automated Brain Tumor Segmentation in MRI Images Using Non-Supervised Model

This study proposes a fully unsupervised brain tumor segmentation framework using multimodal MRI data that enables precise tumor delineation and is suitable for large-scale clinical integration.