An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images
This work addresses medical image classification for breast cancer diagnosis, but it is incremental as it applies standard ensembling to hybrid quantum models.
The paper tackled breast cancer image classification by developing an ensemble of three hybrid classical-quantum neural network architectures, achieving an accuracy of 86.72%, which improved over individual models (best at 85.59%) and classical counterparts.
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.