Abstract
Brain tumors are among the most critical neurological disorders, significantly affecting global health due to their complex pathology and often late diagnosis. Magnetic Resonance Imaging (MRI) is a key diagnostic tool, yet its manual interpretation remains time-consuming and prone to human error. To overcome these issues, we propose NeuroVision-Lite, a lightweight and efficient deep learning framework utilizing MobileNetV3 with transfer learning to detect brain tumors from MRI images. Our approach leverages three sophisticated convolutional neural networks, ConvNeXt-Tiny, MobileNetV3 and EfficientNetB0 architectures, carefully selected and fine-tuned for both performance and efficiency. Extensive experiments demonstrate that our proposed models surpass current state-of-the-art (SOTA) techniques in both accuracy and deployment readiness. Moreover, the proposed model strikes a well-calibrated tradeoff between detection performance and edge-device compatibility. The framework is quantized and exported in deployment-ready formats such as ONNX and PyTorch (*.pt). Furthermore, visual interpretability is enhanced via Grad-CAM-based heatmaps, supporting clinical transparency. The approach aims to support early detection in low-resource and mobile healthcare settings, contributing to improved clinical outcomes through accessible and scalable AI-driven diagnostics.
