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.
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.
Alzheimer's disease (AD) is a debilitating neurodegenerative condition that affects millions globally, with early diagnosis being crucial for effective intervention and treatment. This project explores Quantum Neural Networks (QNN) combined with Magnetic Resonance Imaging (MRI) data to enhance diagnosis accuracy. This hybrid quantum- classical approach leverages advanced preprocessing techniques, uncovering intricate patterns in MRI data. The study highlights the potential of quantum computing in revolutionizing early-stage Alzheimer's detection, achieving superior performance over traditional methods. Keywords— Alzheimer’s disease, Quantum Computing, MRI, Machine Learning, Quantum Neural Networks
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.
Abstract Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.
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.
Dementia is a brain disorder which affects the human memory, behavior and cognitive abilities. The person affected with dementia is unable to perform daily activities. So, there is a need of a model which identifies the Dementia. Many researchers have designed machine learning and deep learning models which uses MRI, clinical data, and medical imaging. The existing models take much time to provide the decision. The Quantum mechine learning algorithm will greatly reduce the execution time. A Quantum Dementia Classification (QDC) model is designed to predict the Dementia disease. The QDC model achieved the accuracy of 95 %. Early detection of Dementia disease can help the doctor to provide more appropriate treatment. The objective of this study is to develop an efficient Quantum Dementia Classification (QDC) model for accurate and early prediction of dementia using clinical and cognitive features. Index Terms-Dementia, Quantum Machine Learning, Quantum Dementia Classification, Prediction.