Accurate Brain Tumor Classification Using MRI Images Based on A Hybrid Vision Transformer and BiLSTM Framework
TL;DR
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.
Abstract
Accurate classification of brain tumors using magnetic resonance imaging (MRI) is essential to clinical diagnosis and treatment. Nevertheless, the wide diversity in a single type of disease and high similarity between the tumors in different categories pose considerable challenges for deep learning models due to the characteristics of CNNs that are mainly for local features extracted, the necessity of reducing these limitations and constraints. This paper discusses an innovative hybrid deep learning paradigm in which an image is modeled by means of a vision transformer (ViT) and a Bi-directional long-term memory network (BiLSTM), resulting in an effective brain tumor classification. The application is based on the framework of ViT, capable of modeling overall context to extract high-level distinguishing features of MRI images, and the BiLSTM successfully capturing sequential dependencies inside of the extracted feature representations. This hybrid architecture is able to be very rich in modelling the spatial and contextual relationships that come with complex medical images. 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. This study demonstrates the efficacy of transformer-based hybrid architectures for medical image analysis with the proposal that by integrating a holistic attention framework with sequential modeling, they can yield substantially better patient diagnosing outcomes. The presented model is not only a viable recommendation for computer assisted diagnostic systems, but is also likely to help with clinical decision making in healthcare fields. The experimental results indicate that the proposed framework maintains a strong balance between accuracy and recall. Specifically, the model achieved accuracy/recall values of 92.5%/92.0% for gliomas, 91.2%/90.7% for meningiomas, and 93.5%/94.0% for brain tumors, resulting in high and consistent F1 scores across all categories.