Medical image classification via quantum neural networks

TL;DR

This paper explores quantum neural network techniques for medical image classification, benchmarking their performance on retinal fundus images and chest X-rays, highlighting potential benefits and hardware limitations.

Contribution

It introduces two quantum neural network approaches for medical imaging and evaluates their effectiveness on different imaging modalities.

Findings

Quantum techniques show promise for medical image classification.

Current hardware limitations affect quantum neural network performance.

Benchmark results demonstrate potential improvements over classical methods.

Abstract

Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification. In recent years, quantum machine learning techniques have been put forward as a way to potentially enhance performance in machine learning applications, both through quantum algorithms for linear algebra and quantum neural networks. In this work, we study two different quantum neural network techniques for medical image classification: first by employing quantum circuits in training of classical neural networks, and second, by designing and training quantum orthogonal neural networks. We benchmark our techniques on two different imaging modalities, retinal color fundus images and chest X-rays. The results show the promises of such techniques and the limitations of current quantum hardware.