Mandapalli Sailaja , M. Sujana Priya Darshini , P Vamsi Krishna Raja 3 3 (July - September) 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 interobserver 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 multiclass brain tumour detection and classification We propose to construct our architecture using the transfer learning paradigm where the CNN backbone is the EfficientNetB0 model and the transformer is the ViTBasePatch16224 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 EfficientNetB0 backbone and the transformer as ViTBasePatch16224 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 Gradientweighted Class Activation Mapping GradCAM to obtain visual explanations so that experts can comprehend and validate predictions made by the model A productionready FastAPI backend and a React frontend 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 stateoftheart baselines on this benchmark dataset with overall classification accuracy of 961 F1score of 958 and ROCAUC of 0992 This ablation study shows the contribution of individual components and demonstrates the effectiveness of the proposed multimodel ensemble with explainability Copyright (c) International Journal of Computational Science and Engineering Research . All rights reserved 2026