Quantum Transfer Learning to Boost Dementia Detection

TL;DR

This paper explores how quantum transfer learning can improve dementia detection by enhancing classical deep learning models, demonstrating increased effectiveness and robustness against noise using biomedical imaging data.

Contribution

It introduces the application of quantum transfer learning to biomedical image classification for dementia detection, showing improvements over classical models and analyzing noise effects.

Findings

Quantum transfer learning enhances dementia classification accuracy.

Quantum techniques transform suboptimal models into more effective solutions.

The approach demonstrates robustness against noise in biomedical data.

Abstract

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.