An Explainable Hybrid CNN–Transformer Framework with Aquila Optimization for MRI-Based Brain Tumor Classification

Abstract Accurate and interpretable brain tumor classification remains a critical challenge due to the heterogeneity of tumor types and the complexity of MRI data. This paper presents a hybrid deep learning framework that synergizes Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for multi-class brain tumor diagnosis. The model leverages CNNs for localized spatial feature extraction and ViTs for capturing long-range contextual information, followed by an attention-guided fusion mechanism. To enhance generalization and reduce feature redundancy, an Improved Aquila Optimizer (AQO) is employed for metaheuristic feature selection. The model is trained and evaluated on the Kaggle brain MRI dataset, comprising 3,264 T1-weighted contrast-enhanced axial slices categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. To ensure interpretability, SHAP and Grad-CAM are integrated to visualize both semantic and spatial relevance in predictions. The proposed method achieves a classification accuracy of 97.2%, F1-score of 0.96, and AUC-ROC of 0.98, outperforming baseline CNN and ViT models. Competing Interest Statement The authors have declared no competing interest. Funding Statement No funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: N/A I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Statement Data is available at https://www.kaggle.com/datasets/pardhut/mri-based-braintumor-pardhu