BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
Researchers have developed BrainFusionNet, a novel deep learning model designed to improve the detection of brain tumors in MRI images. This model integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Gated Recurrent Units (GRUs) to effectively capture local, global, and sequential features within the images. Additionally, BrainFusionNet incorporates explainable AI (XAI) techniques like SHAP, LIME, and GradCAM to visualize and highlight the image regions influencing its diagnostic decisions. Evaluations on public datasets indicate BrainFusionNet achieves 98% accuracy, outperforming existing state-of-the-art CNNs, including DenseNet121 and VGG16, which reached 96% accuracy. AI
IMPACT This hybrid AI model could enhance diagnostic accuracy for brain tumors, potentially leading to earlier and more effective treatment.