A Hybrid Classical-Quantum Convolutional Neural Network for Native-Resolution Multi-Class Blood Cell Classification on BloodMNIST

Abstract

Peripheral blood cell classification is critical for computer-aided haematological diagnosis. In the present study, a hybrid classical-quantum deep learning framework for multi-class classification using the BloodMNIST dataset. Operating directly on native 28×28 images, our pipeline preserves morphological features while minimizing computational complexity. A lightweight convolutional network extracts features that are normalized, reduced via Principal Component Analysis, and encoded into an 8-qubit variational quantum circuit (HybridQNet). The proposed model achieves 96.26% validation and 95.12% test accuracy, demonstrating an efficient, scalable approach for biomedical image analysis.