Brain Tumor Recognition based on Classical to Quantum Transfer Learning

K. T, S. S, Tirumalanadhuni Siva Manikumar, T. Dheeraj, A. Sumanth
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引用次数: 9

Abstract

We expand the idea of transfer learning, generally applied in current machine learning paradigm, to the evolving hybrid neural network contrived out of traditional and quantum components for brain tumor recognition from MRI images. The proposed model is a perfect synergy of traditional classical component and a revolutionary quantum component. The notion of traditional components is to customize a pre-trained network to extract features from brain tumor MRI images, whereas the notion of the quantum components is to employ variational quantum circuit to act as a classifier with learnable parameters. Exhaustive simulation experiments reveals the efficacy of the quantum transfer learning scheme to beat the performance of the conventional classical transfer learning.
基于经典到量子迁移学习的脑肿瘤识别
我们将迁移学习的思想(通常应用于当前的机器学习范式)扩展到由传统和量子组件设计的混合神经网络,用于从MRI图像中识别脑肿瘤。该模型是传统经典分量和革命性量子分量的完美协同。传统组件的概念是定制一个预训练的网络来提取脑肿瘤MRI图像的特征,而量子组件的概念是使用变分量子电路作为具有可学习参数的分类器。详尽的仿真实验表明,量子迁移学习方案的性能优于传统的经典迁移学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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