A quantum convolutional network and ResNet (50)-based classification architecture for the MNIST medical dataset

Biomedical image classification is crucial for both computer vision tasks and clinical care. The conventional method requires a significant amount of time and effort for extracting and selecting classification features. Deep Neural Networks (DNNs) and Quantum Convolutional Neural Networks (QCNN) are emerging techniques in machine learning that have demonstrated their efficacy for various classification tasks. Because of the complexity of their designs, the results of such models may also be challenging to interpret. In this paper, we propose an architecture called Medical Quantum Convolutional Neural Network (MQCNN), based on the QCNN model and a modified ResNet (50) pre-trained model, for enhancing the biomedical image classification in the MNIST medical dataset. During the training phase, the weights are updated using the Adam optimizer, while ResNet (50) is used to reduce the computational cost. MQCNN is compared to the QCNN model, the ResNet (50) pre-trained model, and some other related works on the Medical MNIST dataset. The results showed that MQCNN model achieves 99.6% accuracy, 99.7% precision, 99.6% recall, and 99.7% F1 score, and outperforms the ResNet (50) pre-trained model, the QCNN model, and the other compared related works.