Recent Advances in Energy Efficient Quantum Convolutional Neural Networks with Attention-Based Models for Quality Preservation in WSN assisted IoT Medical Image Diagnostics: A Systematic Review

Abstract

Wireless Sensor Network (WSN)-assisted Internet of Things (IoT) systems have transformed modern healthcare by enabling continuous patient monitoring, remote diagnostics, and real-time medical image transmission. However, limited sensor resources, bandwidth constraints, and noise interference create significant challenges in maintaining energy efficiency and preserving diagnostic image quality. Recent advances in Artificial Intelligence (AI), particularly Quantum Convolutional Neural Networks (QCNNs) integrated with attention-based models, have emerged as promising solutions for addressing these challenges. QCNNs exploit quantum computing principles, including superposition and entanglement, to process high-dimensional medical images with reduced computational complexity and improved feature extraction compared with conventional convolutional neural networks. Furthermore, hybrid quantum-classical architectures enhance learning efficiency and achieve high diagnostic performance even with limited training data. Attention mechanisms complement QCNNs by emphasizing clinically relevant image regions, suppressing noise, and improving feature representation for classification, segmentation, and disease detection. Their integration significantly enhances diagnostic accuracy, transmission efficiency, and energy conservation in resource-constrained WSN environments. This systematic review comprehensively examines recent developments in QCNN and attention-based medical image diagnostics, compares existing methodologies, identifies current research challenges, and outlines future directions for developing scalable, energy-efficient, and intelligent IoT-enabled healthcare systems with improved clinical decision support.