Lightweight CNN-Based Brain Tumor Detection with Visual Interpretability

Early and reliable identification of brain tumors from MRI scans is essential for improving treatment outcomes and reducing mortality. However, conventional deep learning models often require high computational resources and lack transparency, limiting their deployment in real-time clinical environments. This study presents a lightweight CNN-based framework for efficient brain tumor detection integrated with visual interpretability to enhance clinical trust and decision support. The model utilizes a compact convolutional architecture designed for reduced parameters and faster inference, while maintaining high classification accuracy. Grad–CAM–based visual explanations are incorporated to highlight discriminative tumor regions, enabling radiologists to validate and interpret model predictions accurately. Extensive evaluation on publicly available MRI datasets demonstrates that the lightweight CNN achieves superior accuracy with significantly lower computational complexity compared to traditional transfer learning models. The interpretability maps consistently localize pathological areas, confirming the model’s reliability in real diagnostic scenarios. The combination of computational efficiency and transparent decision-making makes the approach well-suited for deployment in resource-constrained healthcare settings, point-of-care diagnostics, and automated screening systems. Overall, this work contributes an interpretable, fast, and robust solution that addresses both performance and clinical usability challenges in brain tumor detection.