Resource-efficient medical image classification for edge devices

TL;DR

This paper presents a resource-efficient approach to medical image classification on edge devices using quantization techniques, enabling real-time diagnostics with reduced model size and latency while maintaining accuracy.

Contribution

It introduces optimized quantization-aware training and post-training quantization methods specifically tailored for medical imaging on resource-constrained edge hardware.

Findings

Quantized models significantly reduce size and inference time.

Models maintain acceptable diagnostic accuracy after quantization.

Enables real-time medical image analysis on edge devices.

Abstract

Medical image classification is a critical task in healthcare, enabling accurate and timely diagnosis. However, deploying deep learning models on resource-constrained edge devices presents significant challenges due to computational and memory limitations. This research investigates a resource-efficient approach to medical image classification by employing model quantization techniques. Quantization reduces the precision of model parameters and activations, significantly lowering computational overhead and memory requirements without sacrificing classification accuracy. The study focuses on the optimization of quantization-aware training (QAT) and post-training quantization (PTQ) methods tailored for edge devices, analyzing their impact on model performance across medical imaging datasets. Experimental results demonstrate that quantized models achieve substantial reductions in model size and inference latency, enabling real-time processing on edge hardware while maintaining clinically acceptable diagnostic accuracy. This work provides a practical pathway for deploying AI-driven medical diagnostics in remote and resource-limited settings, enhancing the accessibility and scalability of healthcare technologies.