Researchers proposed a quantum machine learning (QML) and deep ensemble learning-based approach for Alzheimer’s disease (AD) detection in a recent article in Scientific Reports. AD is a severe neurodegenerative disorder affecting mental abilities, and early diagnosis is crucial for intervention. Structural MRI is a common diagnostic method for AD stages. Deep learning (DL) and ensemble learning have shown promise in disease detection, and QML combines quantum computing and classical ML for improved performance. The study aimed to develop an efficient model for AD classification using ADNI MRI datasets and a combination of DL models and QML classifiers. The proposed model showed improved performance compared to other techniques, with the ensemble model+QSVM achieving the highest accuracy of 99.89 on the merged ADNI dataset. The results validated the effectiveness of the proposed approach for AD detection, outperforming other methods. The study highlights the potential of QML and DL for improving AD diagnosis and the importance of early detection for better patient outcomes. The proposed model offers a reliable solution for AD detection, particularly in cases where MRI scans are unclear, aiding healthcare professionals in accurate disease diagnosis.
Source link
Source link: https://www.azoquantum.com/News.aspx?newsID=10346
Alzheimer’s detection improved by Quantum Machine Learning technology. #healthcare
Preeti
381 Views
0 Votes
GIPHY App Key not set. Please check settings