Enhancing Alzheimer’s Disease Diagnosis Through Deep Learning-Leveraging VGG-16 Architecture and Adaptive Boosting

The key to early intervention and patient care with Alzheimer’s disease is early detection. The World Health Organization estimates that 50 million people worldwide suffer from dementia, with $60-70 \%$ of cases being Alzheimer’s disease (AD). Moreover, the WHO projects that the number of people affected by AD will be nearly tripled by the year 2050, thus, the development of effective detection and management strategies. Our Research suggests a complete method that combines Convolutional Neural Networks (CNNs) with transfer learning using the VGG16 architecture, on the one hand, and Adaptive Boosting on the other, in order to improve the accuracy. The dataset includes NonDemented, VeryMildDemented, MildDemented, and ModerateDemented classes which makes the VGG16 model suitable for extraction of features and classification after the preprocessing steps, such as image resizing, conversion to arrays and pixel value normalization, are performed. From the problems faced in the earlier AD detection methodologies we use CNNs, transfer learning and adaptive boosting to improve the task performance. In conclusion, the combination of automated computational methods and sophisticated algorithms such as CNNs and AdaBoost is a progressive approach in AD detection. It provides services like efficiency, consistency, and improved diagnosis that can be applied to the patient care and the healthcare management.