MobileNet-Lite-Health: A Sustainable Edge AI Framework for Medical Image Classification and Carbon-Aware Computing

Deep learning has transformed medical image analysis, achieving high accuracy in classification, segmentation, and diagnosis. However, the substantial computational and energy demands of advanced models, such as large convolutional neural networks (CNNs) and multimodal vision-language systems, hinder their deployment on resource-constrained edge devices like smartphones, especially in mobile health. This challenge is critical in low-resource, bandwidth-limited settings with limited high-performance computing access, exacerbating healthcare disparities. This paper introduces MobileNet-Lite-Health (MLH), a lightweight CNN framework optimized via quantization, pruning, and knowledge distillation for sustainable medical imaging inference.We assess MLH on MedMNIST (PathMNIST subset) and HAM10000 datasets, representing benchmark and clinical contexts, respectively. Key contributions include: (1) a carbon-aware evaluation pipeline integrating CodeCarbon for CO2 tracking and real-time battery profiling on devices like the Google Pixel 6; (2) comparisons with ResNet-18 and MobileNetV2, showing 74% energy reduction and 76% CO2 decrease per inference with a 2% accuracy drop; and (3) scaling simulations projecting approximately 1.1 g annual CO2 savings per clinic for 50,000 screenings. MLH serves as a green alternative to energy-intensive models like GPT-4V and MedPaLM, unsuitable for edge use. Experimental results confirm MLH’s effectiveness, with 84.7% accuracy on PathMNIST and 80.1% on HAM10000, plus low latency (65 ms) and memory use (95 MB). This work pioneers carbon-aware edge AI, promoting sustainable, accessible healthcare in resource-scarce environments by balancing efficiency, utility, and environmental impact.