Dynamic graph-based quantum feature selection for accurate fetal plane classification in ultrasound imaging

TL;DR

This paper introduces a quantum-based method to improve fetal plane classification in ultrasound imaging, achieving high accuracy and better performance than traditional techniques.

Contribution

The novel DG-QFS framework uses quantum principles and dynamic graphs for feature selection in fetal ultrasound classification.

Findings

The DG-QFS model achieved 96.73% classification accuracy on a fetal plane dataset.

It outperformed baseline deep learning and conventional feature selection methods in accuracy and generalization.

The quantum-driven approach improved interpretability and diagnostic efficiency.

Abstract

Accurate classification of fetal biometric planes in ultrasound imaging is more important for effective prenatal screening and early diagnosis of fetal abnormalities. To enhance the diagnostic efficiency, the research proposed a novel method called “Dynamic Graph-Based Quantum Feature Selection” (DG-QFS) framework to improve the classification performance by integrating the quantum computing principles. Features are extracted from ultrasound images using a pre-trained deep learning model and processed through a quantum-driven feature selection pipeline that models the inter-feature relationships using dynamically entangled multi-qubit graphs. In the DG-QFS method, qubits represent extracted deep feature nodes, while a quantum entanglement score-based dynamic graph captures the complex dependencies. Entanglement score and dynamic graph centrality are used to select the most informative features. The refined feature set is classified using a lightweight multi-layer perceptron (MLP), stochastic gradient descent with adaptive learning rate. Examined the proposed model on a fetal plane ultrasound scan dataset, including 12,400 images of six categorical planes, such as brain, thorax, abdomen, femur, maternal cervix, and other views. Experimental results demonstrate that the proposed model achieves a classification accuracy of 96.73%, significantly outperforming baseline deep learning and conventional feature selection techniques regarding accuracy, generalization, and interpretability.

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