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

Abstract

The most important step in diagnosis, treatment planning, and prediction analysis is the accurate segmentation of brain tumours from magnetic resonance imaging (MRI) scans. Radiologists' manual segmentation was time-consuming, subjective, and inaccurate. For this reason, there is a need for automated approaches. In Current days, deep learning (DL) has shown great promise for enhancing the processing of medical images. In DL, the U-Net architecture has become a typical framework for medical image segmentation of images due to its symmetric encoder–decoder design and skip links that preserve spatial detail. At the same time, conventional U-Net models are restricted to 2D slices and cannot detect contextual connections between slices in spatial MRI images; this may lead to discontinuities and reduced accuracy. The study suggests addressing the above-mentioned challenges, a hybrid deep learning framework consisting of a U-Net architecture integrated with Long Short-Term Memory (LSTM) networks is designed. The U-Net component extracts the variant-invariant spatial and structural features, and LSTM is responsible for integrating the temporal dependencies spatially, which inject discontinuities across parallel slices, which is imperative for the boundary delineation and localisation of the tumour. Compared to the baseline model, the suggested hybrid U-Net and LSTM networks exhibit noticeably better segmentation accuracy and visual consistency under various scenarios after being trained and assessed on a publicly accessible brain MRI dataset. According to experimental data, the suggested model provides great segmentation performance, with Dice scores above 95% and accuracy above 96%. To sum up, the whole exercise displays the promise of combining a convolutional and a recurrent architecture to propagate automated neuroimaging analysis. This will not only reduce the manual labour but also continue to work well in clinical practice, as it is comfortable and prepared for replication in the future.