Brain tumours are among the most deadly cancers and require timely and accurate diagnosis to decide on therapeutic strategies. Manual analysis of Magnetic Resonance Imaging (MRI) scans is fairly time consuming, subjective and prone to inter-observer variation. In the paper, we propose a novel deep learning framework that synergously combines the Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Explainable Artificial Intelligence (XAI) for automated multi-class brain tumour detection and classification. We propose to construct our architecture using the transfer learning paradigm, where the CNN backbone is the EfficientNet-B0 model and the transformer is the ViT-Base-Patch16-224 model, and fuse these two modules using a weighted ensemble. To obtain the proposed architecture, we are suggested to apply transfer learning by using the CNN as EfficientNet-B0 backbone and the transformer as ViT-Base-Patch16-224. Then, we adopt a weighted ensemble fusion strategy to combine these two components. Understanding the problem of class imbalance in the medical imaging datasets, we utilize the Focal Loss which is combined with comprehensive data augmentation technique Albumentations. The brain MRI is classified into 5 classes: Glioma, Meningioma, Pituitary Tumour, No Tumour. Moreover, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM) to obtain visual explanations so that experts can comprehend and validate predictions made by the model. A production-ready FastAPI backend and a React front-end interface are used to deploy the proposed system. We have conducted experiments on one standard brain tumor MRI dataset, and the proposed ensemble model outperforms the standalone CNN and ViT architectures and various state-of-the-art baselines on this benchmark dataset, with overall classification accuracy of 96.1%, F1-score of 95.8% and ROC-AUC of 0.992. This ablation study shows the contribution of individual components and demonstrates the effectiveness of the proposed multi-model ensemble with explainability.
An epidemic survey of recent years has reached a conclusion that virtually 17.9 million lives are lo...
Despite the dynamic nature of the market, large dimensionalities of asset space and varieties of fin...
Although the performance of LLMs on a wide variety of natural language processing problems demonstra...
Web applications are ubiquitous and serve various business and technical functions. As the number of...
