Alzheimer’s disease Detection and Classification using Deep Learning Algorithms

Alzheimer's disease is a significant and growing challenge in the field of healthcare, characterized as a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior. It is caused by complex brain changes that occur over time, including the accumulation of amyloid plaques and tau tangles, which disrupt communication between neurons and ultimately lead to cell death. The impact of Alzheimer's extends beyond the individual, profoundly affecting families and caregivers, as it often results in severe cognitive decline and a loss of independence. Current estimates suggest that millions of individuals are living with Alzheimer's, leading to considerable healthcare costs and emotional burdens. Thus, the urgent need for accurate diagnosis, effective treatments, and supportive care systems is critical for improving the quality of life for those affected and advancing our understanding of this complex disease. Hence, the aim of this proposed study is to address a critical issue in Alzheimer's disease detection by implementing the VGG16 algorithm for feature extraction and EfficientNetB0 and Tiny-CNN for classification. The approach achieves an accuracy of 98.98% in detecting and classifying Alzheimer's disease, providing an effective solution for early diagnosis.