Multi-task Convolutional Neural Network Approach for Automatic Alzheimer Disease Classification

While CNN-based Alzheimer’s disease (AD) classification has showed some encouraging results, it will need high accuracy, fast processing time, and generalizability to a number of population types before it can be used in clinical settings. The brain disorder known as Alzheimer’s disease (AD) is progressive and currently has no cure. Alzheimer’s disease (AD) is characterized by a decline in memory and other mental abilities as well as a loss of autonomy in daily life. The results of this study suggest that using statistical learning and ensemble learning strategies, an image feature extraction technique like the Auto color correlogram Filter methods may be used successfully to an Alzheimer’s photos dataset. Dementia caused by Alzheimer’s disease (AD) is a progressive form of the neurodegenerative brain disorder. It’s not always obvious at first since there are usually so few symptoms at the outset, and they might be similar to those of other disorders. Distinguishing Alzheimer’s disease from mild cognitive deterioration, which may be appreciated in normal aging, may be challenging.