Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays

Abstract

The human respiratory system might be seriously affected by COVID-19 infection. Therefore, early classification of it is a crucial task. Quantum machine learning and quantum neural network models can play an effective role in multiclass classification problems. Compared to standard deep and machine learning classifiers, the quantum variational classifier may lead to less memory usage, accuracy, and portability for respiratory disease detection. This article proposes a hybrid respiratory lung disease detection framework based on classical CNN and Quantum classifiers. It combines a classical deep feature extraction model with quantum classifiers. A new custom convolutional neural network (CCNN) deep learning model is proposed to perform feature extraction, and the Multi-Multi-Single (MMS) & Multi-Single-Multi-Single (MSMS) are proposed as quantum machine learning algorithms. These two quantum classifiers are built via a quantum variational circuit with encoding, entanglement, and measurement properties. The tests were carried out on the COVID-19 Radiography Dataset (CRD), which contains 15,153 chest X-ray images of COVID-19, Viral, and Normal. The experimental results revealed that the proposed model had the highest training and testing accuracy of 98.9% and 98.1%, on the CRD dataset, with a computation cost of 0.07 and 0.08 respectively. This hybrid model performs better than the other standard deep learning models. Additionally, we validated our MMS and MSMS quantum classifiers by deploying them on the IBM Q-QASM real-time quantum computer.