Energy-Efficient Quantum CNN and Attention Models for WSN-Assisted IoT Medical Diagnostics

Abstract

Artificial Intelligence techniques have significantly improved medical image diagnostics in Wireless Sensor Network-assisted Internet of Things healthcare systems by enabling real-time acquisition, transmission, and analysis of MRI, CT, and X-ray images. However, the increasing volume of high-dimensional medical data creates major challenges related to computational complexity, energy consumption, and image quality preservation. Traditional convolutional neural networks provide high diagnostic accuracy but require extensive computational resources, making them less suitable for resource-constrained IoT healthcare environments. To address these limitations, Quantum Convolutional Neural Networks have emerged as promising alternatives by utilizing quantum principles such as superposition and entanglement to improve computational efficiency while reducing resource requirements. These models demonstrate lower computational complexity, improved robustness, and enhanced energy efficiency compared to conventional CNN architectures. Attention-based models further enhance diagnostic performance by enabling selective feature extraction and focusing on clinically relevant regions within medical images. Quantized and self-attention frameworks significantly reduce computational cost and energy consumption, making them suitable for IoT-based healthcare applications. Hybrid quantum-classical architectures integrating attention mechanisms combine the strengths of quantum computing and deep learning to improve both accuracy and efficiency. Overall, energy-efficient QCNNs with attention-based learning provide a promising framework for scalable, intelligent, and high-quality medical image diagnostics in future IoT healthcare systems.