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.
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.
Wireless Sensor Network (WSN)-assisted Internet of Things (IoT) systems have revolutionized modern healthcare by enabling real-time medical image acquisition, transmission, and remote diagnostics. However, these systems face critical challenges such as limited energy resources, bandwidth constraints, and degradation of image quality during transmission and processing. Ensuring energy-efficient computation while preserving diagnostic image quality is therefore essential for reliable medical decision-making. Artificial Intelligence (AI)-driven approaches, particularly Quantum Convolutional Neural Networks (QCNNs) integrated with attention-based models, have emerged as promising solutions to address these challenges. Quantum Convolutional Neural Networks leverage principles of quantum computing such as superposition and entanglement to process high-dimensional data more efficiently than classical convolutional neural networks. QCNN architectures can significantly reduce model parameters and computational complexity while maintaining high accuracy, making them suitable for resource-constrained WSN environments. Studies indicate that QCNNs can outperform classical CNNs in terms of efficiency and scalability, especially in complex image classification tasks. Additionally, hybrid quantum-classical models further enhance performance by combining classical feature extraction with quantum optimization capabilities. Attention-based models play a vital role in improving medical image diagnostics by focusing on relevant regions of interest and suppressing noise. Deep learning techniques, particularly CNN-based models, have demonstrated superior performance in medical image understanding tasks such as classification, segmentation, and detection. Attention mechanisms enhance these capabilities by improving feature representation and preserving critical diagnostic information, thereby improving overall image quality.
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.
The rapid advancement of Wireless Sensor Networks (WSNs) and Internet of Things (IoT) technologies has transformed modern healthcare by enabling continuous acquisition, transmission, and intelligent analysis of medical data. In medical image diagnostics, these technologies support remote monitoring, early disease identification, and timely clinical decision-making. However, transmitting and processing high-dimensional medical images through resource-constrained WSN-assisted IoT environments introduces challenges related to energy consumption, bandwidth limitations, computational complexity, and image-quality preservation. Conventional Convolutional Neural Networks (CNNs) provide strong diagnostic performance but often demand substantial computational resources, limiting their suitability for energy-constrained healthcare devices. Quantum Convolutional Neural Networks (QCNNs) have consequently emerged as a promising computational paradigm for efficient medical image analysis. By exploiting quantum principles such as superposition and entanglement, QCNNs offer opportunities for efficient feature representation and reduced computational overhead. Integrating attention mechanisms can further strengthen diagnostic performance by emphasizing clinically significant image regions while suppressing irrelevant information. This review examines energy-efficient QCNNs integrated with attention-based models for quality-preserving medical image diagnostics in WSN-assisted IoT environments. It critically evaluates recent architectures, energy-efficiency strategies, diagnostic performance, and quality-preservation methods while identifying existing limitations and research gaps. The review further highlights future directions toward scalable, intelligent, and resource-efficient IoT healthcare diagnostic frameworks.
Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.
Quantum-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.
Quantum computing holds considerable promise for artificial intelligence (AI) in clinical decision support systems (CDSS), particularly in resource-constrained environments. This paper investigates a multiqubit quantum convolutional neural network (MQ-CNN) for medical diagnostics, leveraging parameterized quantum circuits to process low-resource healthcare datasets. We evaluate the framework on three binary classification tasks: breast cancer (Wisconsin dataset), diabetes (Pima Indians), and heart failure prediction, using angle-encoded clinical features. The MQ-CNN achieves test accuracies of 82.4%, 98.7%, and 97.3% respectively, matching classical CNNs while reducing trainable parameters significantly. Comparative analysis shows the quantum model converges 22% faster than hybrid quantum-classical counterparts under identical training conditions. Robustness evaluations confirm ≤3 % accuracy degradation when subjected to 15% synthetic label noise. These results highlight the architecture's suitability for resource-constrained environments, demonstrating that quantum-enhanced feature extraction can maintain diagnostic accuracy while significantly reducing computational overhead. This work provides empirical evidence for near-term quantum machine learning in practical healthcare applications.