Hybrid Neural Networks for Medical Image Classification

The paper explores the integration of artificial intelligence (AI) methods, particularly neural networks, and quantum computing for medical applications. The study aims to evaluate the feasibility of quantum convolutional (“quanvolutional”) neural networks (QNNs) for multi-class classification, comparing them with traditional fully-connected neural networks (FCNNs) and convolutional neural networks (CNNs) on standard and medical datasets. Various quantum transformations are applied, and the models are trained and validated using CIFAR10 and BloodMNIST datasets . The performance metrics, including accuracy and loss, are analyzed, revealing insights into the effectiveness of QNNs compared to classical models . Overall, the study highlights the promising potential of quantum-enhanced machine learning methodologies in medical applications but calls for further research to optimize performance across diverse datasets and problem domains.