An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images

TL;DR

This paper explores hybrid classical-quantum convolutional neural networks for breast cancer image classification, demonstrating that ensemble methods improve accuracy over individual models and classical counterparts.

Contribution

It introduces and evaluates three hybrid classical-quantum neural network architectures combined through ensembling for medical image classification.

Findings

Ensemble methods increased accuracy to 86.72%.

Hybrid quantum-classical models outperform classical neural networks.

Quantum models show promise despite NISQ era challenges.

Abstract

Quantum neural networks are deemed suitable to replace classical neural networks in their ability to learn and scale up network models using quantum-exclusive phenomena like superposition and entanglement. However, in the noisy intermediate scale quantum (NISQ) era, the trainability and expressibility of quantum models are yet under investigation. Medical image classification on the other hand, pertains well to applications in deep learning, particularly, convolutional neural networks. In this paper, we carry out a study of three hybrid classical-quantum neural network architectures and combine them using standard ensembling techniques on a breast cancer histopathological dataset. The best accuracy percentage obtained by an individual model is 85.59. Whereas, on performing ensemble, we have obtained accuracy as high as 86.72%, an improvement over the individual hybrid network as well as classical neural network counterparts of the hybrid network models.