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Utilitas Mathematica

Analyzing Quantum CNN and Quantum SVM for Alzheimer's Diagnosis

Fazil A. Sheikh, Dr. S.Y. Amdani, Dr. Suresh Asole

Published Jun 16, 2025• 0 citations
OpenAlex

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.

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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.
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.
Alzheimer's Disease (AD) first affects the brain parts that are associated with learning. It is one of the most prevalent forms of Dementia. Its early diagnosis is crucial for properly managing the disease treatment because it is chronic and irreversible. This paper introduces a holistic approach to achieve early detection of AD using the hippocampus and transfer learning. Here we have compared the results from the classical machine learning model – Support Vector Machine (SVM) and the Quantum Support Vector Machine (QSVM) model. QSVM - a quantum variant of standard SVM algorithm. It is developed using techniques such as quantum kernel estimation in quantum computing for more efficient processing of high-dimensional data than standard SVMs.. The most common symptoms of Alzheimer’s are loss of memory and cognitive impairment. This stems from the destruction and death of nerve cells in the brain related to memory. Mild Cognitive Impairment (MCI) is a condition between normal brain function and Alzheimer’s. From the prodromal MCI stage, it progresses gradually to dementia. Several studies show that Alzheimer’s develops at a rate of 10–15% per year from MCI. The early identification of patients with MCI may halt/delay the progression from MCI stage to Alzheimer’s. The results with the precision of 0.85 with the QSVM compared to the precision of 0.78 and recall of 1.00 compared to the 0.89 with the traditional SVM shows that our technique of Quantum Machine Learning (QML) is very useful in Alzheimer’s early diagnosis.
Alzheimer’s is a progressive and degenerative brain disorder causing impairment in memory and cognitive function. It is the most common form of Dementia and the prevalence of Alzheimer’s disease (AD) increases with each decade of life. This usually starts in late middle age to old age resulting in progressive memory loss, impaired thinking, disorientation and changes in personality. Neurons are first lost in the hippocampus (the brain’s centre for memory and learning), and presence of neurofibrillary tangles and plaques containing beta-amyloid cells. Owing to the progressive nature of the disease and the complex structure of the brain, the exact causes, mechanisms and how Alzheimer’s spread is not completely understood thereby inhibiting in the production of a cure for the disease. Quantum Machine Learning (QML) is a promising area of research for the detection and diagnosis of Alzheimer’s disease. PET scan helps in diagnosing Alzheimer’s by detecting the amyloid plaques and tau tangles. It reveals areas with reduced metabolism, which helps differentiate Alzheimer’s from other conditions. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. In this study, we propose a model based on quantum machine learning classifiers to classify Alzheimer’s disease. This showcases the feasibility of Quantum enhanced classification and detection of AD for extremely limited medical datasets.
‘Quantum Convolutional Neural Networks’ - QCNN is quantum machine learning model that has the potential of improving the accuracy of Alzheimer’s Disease (AD) diagnosis. Alzheimer’s disease - a neurodegenerative disorder - is considered to be the major cause of Dementia. Early diagnosis of AD is critical for improving patient outcomes. However, current diagnostic methods are often less accurate and time-consuming. QCNNs are able to learn complex features from data, which could be used to identify early signs of AD thereby resulting in an early diagnosis of a disease. This proposed technique will investigate the use of QCNN for AD diagnosis. A QCNN will be trained on a large image dataset from healthy controls & AD patients. The QCNN will then be fine-tuned on a smaller image datasets of patients with suspected AD. The performance of the QCNN will be evaluated on a test dataset of images from patients with suspected AD. This work attempts to design and develop a technique for early disease diagnosis. The QCNN could achieve 88% accuracy in classifying the data(2022) Li et al.[2]. They used a QCNN to diagnose AD from brain functional connectivity data. In another study, by Chen et al.[3], a QCNN could achieve 90% accuracy in classifying the images (2022). So taking inspiration from this, we are proposing to fine tune the QCNN in such a way as to further improve its performance and thereby resulting in more accuracy. The results of the proposed work would highlight the potential of QCNN for AD early diagnosis. If the results are promising, QCNN could be a valuable tool for early diagnosis of Alzheimer’s disease and thereby assists in saving millions of lives.
Quantum machine learning is a new research field combining quantum information science and machine learning. Quantum computing technologies appear to be particularly well-suited for addressing problems in the health sector efficiently. They have the potential to handle large datasets more effectively than classical models and offer greater transparency and interpretability for clinicians. Alzheimer’s disease is a neurodegenerative brain disorder that mostly affects elderly people, causing important cognitive impairments. It is the most common cause of dementia and it has an effect on memory, thought, learning abilities and movement control. This type of disease has no cure, consequently an early diagnosis is fundamental for reducing its impact. The analysis of handwriting can be effective for diagnosing, as many researches have conjectured. The DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset contains handwriting samples from people affected by Alzheimer’s disease and a group of healthy people. Here we apply quantum AI to this use-case. In particular, we use this dataset to test classical methods for classification and compare their performances with the ones obtained via quantum machine learning methods. We find that quantum methods generally perform better than classical methods. Our results pave the way for future new quantum machine learning applications in early-screening diagnostics in the healthcare domain.
Alzheimer’s disease is neurodegerative disorder that affects memory of a person and also the day to day activities of them. Normally, Alzheimer’s disease can be identified by using the MRI scans which consist of high dimensional data. Traditional machine learning models are filed to give higher accuracy while diagnosing the disease. Quantum technologies plays a vital role and its emerging technologies that produce higher accuracy compared to traditional models. In this study we provide a comprehensive review of quantum technology such as Quantum Machine Learning (QML), Quantum Neural Networks (QNN) and Hybrid Quantum Models. Key factors of this study include early identification of Alzheimer’s disease by utilizing the higher dimension of data like MRI images. Further, this article focuses on the application, limitations such as hardware constraints and scalability of clinical decision support system.