Automated classification and segmentation of brain abnormalities using deep learning approaches in brain MRI

Abstract Background Accurate classification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) are essential for early diagnosis, treatment planning, and clinical decision-making. However, manual interpretation of MRI scans is time-consuming and prone to inter-observer variability. Deep learning techniques have emerged as effective solutions for automating brain tumor analysis with improved accuracy and reliability. Methods This study proposes a unified deep learning framework that integrates ensemble-based brain tumor classification, U-Net-based tumor segmentation, and Grad-CAM explainability for automated brain MRI analysis. A soft-voting ensemble of DenseNet121 and InceptionV4 was developed for multi-class tumor classification, while a U-Net architecture was employed for tumor segmentation. The classification and segmentation models were evaluated on publicly available brain tumor MRI and BraTS 2020 datasets using standard performance metrics. Results The proposed ensemble classification model achieved an accuracy of 98.52%, outperforming the individual classification models, while the U-Net segmentation model achieved a Dice score of 0.8423 and an Intersection over Union (IoU) of 0.7497. Grad-CAM visualisations further improved model interpretability by highlighting clinically relevant tumor regions. Conclusion The proposed framework demonstrates the effectiveness of integrating ensemble classification, tumor segmentation, and explainable artificial intelligence into a unified diagnostic pipeline. These findings highlight the potential of deep learning-based computer-aided diagnosis systems to support accurate, reliable, and interpretable brain tumor diagnosis and clinical decision-making. The source code is publicly available on GitHub at: https://github.com/Rashmitha-R-Nayak/brain-tumor-project .