Investigation of Applying Quantum Neural Network of Early-Stage Breast Cancer Detection

TL;DR

This paper explores the potential of quantum neural networks (QNN) for early breast cancer detection, aiming to establish a baseline for their effectiveness in large-scale screening using quantum computing.

Contribution

It provides a foundational assessment of QNN's viability for mass-scale early breast cancer detection, highlighting its promising potential.

Findings

QNN shows potential for effective mass-scale screening

Baseline results indicate promising opportunities for QNN in diagnostics

Quantum computing can enhance early cancer detection methods

Abstract

Due to the heavy burden on medical institutes and computer-aided image diagnostics (CAD) have been gaining importance in diagnostic medicine to aid the medical staff to attain better service for the patients. Breast cancer is a fatal disease that can be treated successfully if it is detected early. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, we are trying to answer the question of: whether can the QNN be an effective method for mass-scale early breast cancer detection. This paper is dedicated to drawing a baseline for examining QNN, and the results showed a promising opportunity to use it for mass-scale screening using a fully functional quantum computer.