Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Quantum Computing · Depth: Expert, quick

Summary

A study submitted on May 22, 2026, and revised on July 22, 2026, investigates Hybrid Quantum-Classical Neural Networks (HQNNs) to improve microscopic blood cell classification. The proposed modular architecture combines a pre-trained ResNet-50 backbone with a low-dimensional latent bottleneck and a variational quantum circuit. This design allows for direct comparison between quantum-enhanced and purely classical transformation mechanisms. Researchers evaluated three architectures: the HQNN model, a Classical Matched Model with comparable capacity, and a baseline without an intermediate transformation. Experiments on two public datasets, Blood Cell Images and PBC, showed HQNNs consistently achieved superior or more balanced performance. Specifically, the approach improved macro F1-score by up to 3.7% on the Blood Cell Images Dataset and increased F1-score from 98.54% to 98.69% in a challenging 8-class scenario. Evaluation on IBM quantum hardware confirmed the model's robustness, showing only modest performance degradation due to noise.

Key takeaway

For AI Scientists and Research Scientists developing medical image analysis solutions, consider integrating Hybrid Quantum-Classical Neural Networks. Your models could achieve superior classification performance, particularly in scenarios with subtle variations or limited data, as demonstrated by up to a 3.7% macro F1-score improvement. This approach offers robust performance even when deployed on noisy quantum hardware, suggesting a practical path for enhancing diagnostic accuracy in critical medical imaging tasks.

Key insights

Hybrid Quantum-Classical Neural Networks enhance blood cell classification by improving feature representation, even under quantum noise.

Principles

• Quantum feature transformations can boost discriminative representations.
• HQNNs offer balanced performance across various metrics.
• Modest quantum noise does not severely degrade HQNN performance.

Method

Combine a pre-trained ResNet-50 with a latent bottleneck and a variational quantum circuit for feature transformation in classification tasks.

In practice

• Apply HQNNs to medical imaging tasks requiring subtle feature distinction.
• Use HQNNs for challenging multi-class classification problems.

Topics

• Hybrid Quantum Neural Networks
• Blood Cell Classification
• Medical Image Analysis
• ResNet-50
• Variational Quantum Circuits
• Quantum Machine Learning

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.