Quanvolutional Neural Networks for Pneumonia Detection: An Efficient Quantum-Assisted Feature Extraction Paradigm

2025-10-26

AI Summary

To address the high computational cost, limited feature representation capability, and poor generalization under small-sample conditions in convolutional neural networks (CNNs) for pneumonia diagnosis from X-ray images, this paper proposes a hybrid quantum-classical neural network. The core innovation is a “quanvolutional” layer based on parameterized quantum circuits (PQCs): it encodes 2×2 image patches into quantum states and performs non-classical feature extraction via Y-rotation and entangling gates. This layer is embedded within a classical CNN backbone to form an end-to-end trainable architecture. Evaluated on the PneumoniaMNIST dataset, the model achieves a validation accuracy of 83.33%, substantially outperforming an equivalent classical CNN (73.33%), while converging faster and exhibiting higher sample efficiency. Results demonstrate that quantum-assisted feature encoding enhances discriminative power and generalization capability in medical image analysis under data-scarce conditions.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection, CNNs often suffer from high computational costs, limitations in feature representation, and challenges in generalizing from smaller datasets. To address these limitations, we explore the application of Quanvolutional Neural Networks (QNNs), leveraging quantum computing for enhanced feature extraction. This paper introduces a novel hybrid quantum-classical model for pneumonia detection using the PneumoniaMNIST dataset. Our approach utilizes a quanvolutional layer with a parameterized quantum circuit (PQC) to process 2x2 image patches, employing rotational Y-gates for data encoding and entangling layers to generate non-classical feature representations. These quantum-extracted features are then fed into a classical neural network for classification. Experimental results demonstrate that the proposed QNN achieves a higher validation accuracy of 83.33 percent compared to a comparable classical CNN which achieves 73.33 percent. This enhanced convergence and sample efficiency highlight the potential of QNNs for medical image analysis, particularly in scenarios with limited labeled data. This research lays the foundation for integrating quantum computing into deep-learning-driven medical diagnostic systems, offering a computationally efficient alternative to traditional approaches.

Problem

• Detect pneumonia using quantum-enhanced feature extraction
• Address computational limitations of classical neural networks
• Improve medical image analysis with limited datasets

Innovation

• Hybrid quantum-classical model for pneumonia detection
• Quanvolutional layer with parameterized quantum circuits
• Quantum feature extraction enhances classical neural networks