Title:Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset
Volume: 20
Author(s): Tayyaba Shahwar, Fatma Mallek, Ateeq Ur Rehman*, Muhammad Tariq Sadiq and Habib Hamam
Affiliation:
- School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Keywords:
Machine learning, Deep learning, Transfer learning, Convolutional neural network, Pre-trained model, Quantum computing, Quantum variational circuit, Quantum neural network, Hybrid model, Pneumonia detection, Chest X-rays.
Abstract:
Introduction:
Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects
of diagnosis and planning can be further enhanced through the implementation of a Quantum Deep Neural Network (QDNN). Therefore, we
introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting
pneumonia using 5863 X-ray scans with binary cases.
Methods:
The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These
significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit
algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a
deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices
to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10−6 and six
layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs.
Results:
The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy
gained by the Convolutional Neural Network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a
variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method.
Conclusion:
This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the
speed and accuracy of the network in the clinical and healthcare sectors.
