Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
Deep learning models have shown strong performance in medical image analysis, but deploying them in low-resource clinical environments remains difficult due to computational, memory, and power constraints. This paper presents a multi-strategy compression framework for brain tumor classification from MRI, encompassing quantization-aware training, knowledge distillation from a DenseNet-101 teacher to a compact DenseNet-32 student with low-bit post-training quantization, and Float16 post-training quantization on a lightweight MobileNetV2 backbone. Using a multi-class brain tumor MRI dataset containing glioma, meningioma, pituitary tumors, and healthy controls, we provide full experimental va...
| Authors | Sumanth Meenan Kanneti, Aryan Shah |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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