Alzheimer’s Disease Detection using Deep Learning Algorithm

Alzheimer’s disease is a neurological disorder. Research on early detection and classification using machine learning techniques has become essential in recent years. This paper focuses on developing a Convolutional Neural Network for early-stage Alzheimer’s disease detection from brain MRI scans. Aim to create a highly accurate and robust model with sensitivity and specificity for identifying Alzheimer’s, mild cognitive impairment, and healthy individuals. The research utilizes the OASIS Alzheimer’s image dataset as the input data set. CNN-based preprocessing, feature extraction, extensive training, fine-tuning, data augmentation, and cross-validation on these datasets ensure the model’s reliability. The proposed prediction model achieves the precision values for each class ranging from 0.209 for mild demented to 0.255 for moderate demented.