Alzheimer's disease classification for MRI images using Convolutional Neural Networks

Alzheimer's disease (AD) is the main cause of dementia and memory loss. Dementia has an important effect on the global healthcare system and society. Most cases of Alzheimer's disease occur in people around the age of sixty; however, it could be a result of concussions or other injuries to the brain. Alzheimer's disease requires a rapid diagnosis to begin effective treatment. The importance of early Alzheimer's disease diagnosis for therapeutic development and, finally effective patient care can really be affected. Current developments in deep learning provide promising findings for detecting different medical scans. This paper presented a CNN model for detecting Alzheimer's disease using MRI images. The suggested CNN model is extensively compared with several pretrained models, such as VGG16,VGG19,DenseNet201,ResNet101, Inceptionv3, MobileNet,Inception-ResNetV2, DenseNet169, and DenseNet121. Also, the evaluation of the proposed model is compared to relevant research efforts; findings indicate that it outperforms well, with an accuracy rate of 97%.