Innovative Transfer Learning Strategies for Alzheimer's Detection Using Deep Learning

The progressive neurodegenerative disorder known as Alzheimer's disease reduces cognitive abilities, therefore influencing the interpersonal quality of living for people impacted. Early identification is absolutely vital since it enables timely treatments and management, therefore enhancing patient outcomes and enabling focused therapy approaches. This paper uses a VGG-16 transfer learning model to investigate ways to raise the diagnosis accuracy for Alzheimer's disease. The dataset used consists in three subsets: 27,187 pictures for learning, 6,497 pictures for evaluation, and 2,039 pictures for testing, each labelled with four classes: MildDemented, VeryMildDemented, NonDemented, and ModerateDemented. The model was developed using the training data; the validation set served to tune the model and reduce overfitting. Efficiency of the model on unprocessed data was evaluated using the test set. Our results show that the VGG-16 transfer learning model obtained a remarkable 95% accuracy. This great degree of performance emphasizes the efficiency of the model in early Alzheimer's diagnosis and encourages developments in health innovation, thereby improving healthcare outcomes and promoting inclusivity in medical diagnostics. These advancements contribute towards Sustainable Development Goals (SDGs), particularly in the areas of Good Health and Well-being, Reduced Inequalities, Industry, Innovation, and Infrastructure, and Partnerships for the Goals, thereby fostering equitable and accessible healthcare solutions for all.