Analyzing Quantum CNN and Quantum SVM for Alzheimer's Diagnosis

Abstract

Alzheimer's disease affects our thinking and behavior. It is one of the types of dementia. It initially affects the brain’s learning regions. Since the disease is both progressive and irreversible, early diagnosis is vital for effective treatment management. This research presents a comprehensive, end-to-end approach for early detection of Alzheimer’s by focusing on the hippocampus and leveraging transfer learning. The study compares the performance of a Quantum Convolutional Neural Network (QCNN) with that of a Quantum Support Vector Machine (QSVM). Findings suggest that QCNN is more efficient at handling high-dimensional data compared to QSVM. Alzheimer's disease is mainly defined by the deterioration of memory and cognitive abilities, which results from the degeneration and death of neurons that play a crucial role in memory function. Mild Cognitive Impairment (MCI), which sits between normal cognitive function and Alzheimer’s, offers a critical window for early intervention. Diagnosing MCI early may help delay or even prevent the onset of Alzheimer’s. The results of our experiment show that the QCNN model achieved a precision of 0.88, recall of 0.96, and an accuracy of 0.59. In contrast, the QSVM model recorded a precision of 0.85, recall of 1.00, and an accuracy of 0.85. These findings indicate that both quantum machine learning models demonstrate strong potential and complement each other in performance.