Enhancing Alzheimer's Disease Prediction through Deep Learning Models: A Comparative Study of GoogLeNet, LeNet, and UNet

Alzheimer's disease represents a major medical challenge, changing slowly over a long period of memory and cognitive abilities. A key problem in effectively addressing Alzheimer's is its early detection. Manual diagnosis, often reliant on subjective interpretation of symptoms and medical tests, can lead to errors and delays. These challenges emphasize the urgent requirement for diagnostic tools that are both more precise and more efficient. To address these challenges, turn to deep learning, and borrow advanced techniques like Convolutional Neural Networks (CNNs) to enhance Alzheimer's detection. In this research, the efficacy of CNN architectures such as GoogLeNet, LeNet, and UNet was explored in analyzing brain scan data for early signs of the disease. The research findings reveal that the CNN model achieved the highest accuracy, scoring 0.99, followed by LeNet with an accuracy of 0.97, UNet at 0.94, and GoogLeNet at 0.51. These emphasize the promise of deep learning to enhance the diagnosis and treatment of Alzheimer's disease and make early interventions and personalized care easier. The promising outcomes from CNN models highlight their capability to transform the clinical approach to Alzheimer's, emphasizing the importance of technological advancements in addressing this debilitating condition.