Automated brain tumor classification from magnetic resonance imaging (MRI) can support accessible medical image analysis, but conventional deep learning models often exceed the resource budgets of highly constrained edge-AI platforms. We present NanoNet, an ultra-lightweight convolutional neural network designed for resourceconstrained MRI classification and motivated by future edge-AI applications. Inspired by MobileNetV2, NanoNet substantially reduces network depth, channel capacity, expansion ratios, and final feature dimensionality, resulting in only five inverted residual blocks and 8,132 parameters. We further investigate dependency-aware structured pruning, knowledge distillation, and INT8 quantization-aware training for additional compression. Experiments on the BRISC 2025 benchmark show that NanoNet achieves 92.80% accuracy and 92.97% macro F1 with 8,132 parameters and 10.498 MFLOPs. Across three compression seeds, it exceeds the mean accuracy of 70%-pruned MobileNetV2 by 5.20 percentage points while using 60.2% fewer parameters and 9.8% less computation. Converted INT8 NanoNet achieves 89.70% accuracy and 90.22% macro F1 across three QAT seeds. Earlier TensorFlow Lite measurements indicate 27.7 KB of baseline model storage, motivating future edge-AI deployment.

翻译：暂无翻译