MHMamba: Mobile Hybrid Model for Edge-Enabled Acromegaly Auxiliary Diagnosis in Smart Healthcare

Abstract

Acromegaly, a chronic endocrine disorder, requires early diagnosis to prevent severe complications. Existing deep-learning-based diagnostic methods often prioritize accuracy at the expense of computational complexity and feature diversity, limiting their deployment in resource-constrained IoT environments. This article proposes Mobile Hybrid Mamba (MHMamba), a lightweight model integrating inverted residual attention convolution (IRAC) and VMamba, designed for efficient acromegaly screening on edge devices. By synergizing convolutional neural network’s local feature extraction with VMamba’s linear-complexity global modeling, MHMamba achieves state-of-the-art performance (2.8%–3.1% improvement in precision, recall, and F1-score) while maintaining ultralightweight parameters (13.882M) and low computational overhead (2.054G FLOPs). Crucially, MHMamba’s mobile-friendly architecture enables real-time facial analysis on smartphones, making it suitable for IoT-driven telemedicine and decentralized health monitoring. Through occlusion experiments and GradCAM++ visualization, we identify key facial regions (nose, mouth, and cheekbones) critical for diagnosis, aligning with clinical biomarkers. The model’s interpretability and portability position it as a pivotal tool for IoT-enabled smart healthcare systems, bridging the gap between artificial-intelligence-driven diagnostics and edge device deployment.