Resource-efficient medical image classification for edge devices
It addresses the problem of enabling AI-driven medical diagnostics in remote and resource-limited settings, enhancing accessibility and scalability, though it is incremental as it applies existing quantization methods to medical imaging.
This research tackled the challenge of deploying deep learning models for medical image classification on resource-constrained edge devices by using model quantization techniques, achieving substantial reductions in model size and inference latency while maintaining clinically acceptable diagnostic accuracy.
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.