Performance Evaluation of Quantum CNN and Classical CNN for Alzheimer's Diagnosis
Keywords:
Alzheimer’s Diagnosis,QCNN, CNN, Quantum ML, Machine LearningAbstract
Alzheimer’s Disease (AD) first strikes learning regions of the brain and is one of the most prevalent forms of dementia. Early diagnosis is crucial for optimum management of treatment, as the disorder is both irreversible and progressive. The current research outlines an end-to-end approach toward early diagnosis of AD by utilizing the hippocampus and transfer learning. The results of a conventional machine learning model—Convolutional Neural Network (CNN)—are compared to those of a Quantum Convolutional Neural Network (QCNN), an adaptation of the conventional CNN through the use of quantum computing methods like quantum kernel estimation. QCNN achieves greater efficiency in processing high-dimensional data than traditional CNNs.
The main symptoms of Alzheimer’s are memory loss and cognitive impairment, caused by the degeneration and death of those neurons related to memory. Mild Cognitive Impairment (MCI) lies between cognition within the range of normality and Alzheimer’s. The early diagnosis of MCI has the potential to decelerate or even prevent the development of Alzheimer’s. In this paper, we have established that the QCNN model reached precision at 0.88 and recall at 0.96, compared to the classic CNN, where precision at 0.80, and recall at 0.84 have been achieved. The results highlight the prospective ability of Quantum Machine Learning (QML) to diagnose Alzheimer’s Disease at an early stage.
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