Detection of Alzheimer's Disease using deep learning algorithm

Abstract

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting a significant portion of the elderly. Timely and accurate detection of AD is crucial for effective management and intervention. Deep learning algorithms have shown promising results in medical image analysis, including diagnosing AD using magnetic resonance imaging (MRI) scans. This study aims to compare the performance of various deep learning architectures, namely CNN (Convolutional Neural Network) for AD detection on MRI images. A large dataset comprising MRI scans from AD patients and healthy controls is utilized for model training and evaluation. The deep learning models are trained to automatically learn discriminative features from the MRI images. Performance evaluation metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are used to assess and compare the models' performance. This study provides insights into the suitability to show the ability of preference of this algorithm. The findings can aid in selecting the most appropriate algorithm for AD diagnosis based on specific requirements, such as accuracy, computational efficiency, and resource availability. Further investigation and validation on more extensive and diverse datasets are necessary to establish the generalizability and clinical viability of these algorithms for AD detection in real-world settings.