A Lightweight Hybrid Deep Learning Approach for Multi-Class Brain Tumor Classification

Brain tumors are a significant health concern, requiring timely and accurate diagnosis to improve treatment outcomes. Magnetic Resonance Imaging (MRI) is a widely used technique for brain tumor detection, but manual diagnosis is time-consuming and prone to errors. This study proposes a hybrid deep learning model inspired by VGG16, ResNet50, and EfficientNetB3 architectures for multi-class brain tumor classification. The aim is to develop an efficient, high-performance model capable of classifying brain MRI scans into four categories: glioma, meningioma, pituitary tumors, and non-tumorous cases. The proposed model was trained and evaluated on a publicly available dataset from Kaggle, containing 7,023 MRI images. The hybrid model achieved a high accuracy of 98.77%, with weighted precision, recall, and F1-scores all also recorded at 98.77%. Additionally, the model’s compact size of just 2.20 MB ensures computational efficiency and adaptability, making it suitable for resource-constrained environments. Compared to existing classification methods, which require higher storage and computational resources, the proposed model offers improved performance with minimal resource requirements. This research demonstrates the potential of hybrid deep learning models for medical image classification, providing a promising solution for faster and more accurate brain tumor diagnosis. The findings suggest that the model’s compact size and high accuracy could significantly enhance clinical workflows and decision-making processes in medical imaging.