A Float16-quantized MobileNetV2 model for multi-class brain tumor MRI classification reaches 82.37% validation accuracy while shrinking from 35.34 MB to 5.76 MB, a 6.14x reduction with negligible accuracy change.
Densely connected convolutional networks,
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Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
A Float16-quantized MobileNetV2 model for multi-class brain tumor MRI classification reaches 82.37% validation accuracy while shrinking from 35.34 MB to 5.76 MB, a 6.14x reduction with negligible accuracy change.