Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks
Researchers have developed a Hybrid Quantum-Classical Neural Network (HQNN) architecture to improve the classification of blood cells in medical images. This approach combines a ResNet-50 backbone with a variational quantum circuit, demonstrating superior performance compared to purely classical models. Experiments showed a 3.7% improvement in macro F1-score on one dataset and a slight increase in F1-score on a more challenging 8-class scenario. The HQNN model also proved robust when tested on actual IBM quantum hardware, indicating practical potential for medical imaging tasks. AI
IMPACT Quantum-enhanced neural networks show promise for improving accuracy in specialized medical image analysis tasks.