# An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images Source: [An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:40:00.946Z Authors: Dibyasree Guha, Shyamali Mitra, Somenath Kuiry, Nibaran Das Published: 2024-09-24T10:43:27.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()