Quantum Transfer Learning to Boost Dementia Detection
This work addresses the problem of early and accurate dementia detection for healthcare systems, but it is incremental as it builds on existing quantum machine learning methods applied to a specific biomedical task.
The paper tackled the challenge of improving dementia detection from biomedical images by applying quantum transfer learning to a weak classical deep learning model, resulting in enhanced performance for binary classification on the OASIS 2 dataset.
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.