Alzheimer’s Disease Classification Using Deep Convolutional Neural Network

Alzheimer’s Disease is the most common form of dementia which initially destroys the memory and finally progresses to death. This irreversible disease is mostly found among older people. The latest innovations on the multimodal neuroimaging data made it possible to detect the disease in life which was a major breakthrough in neuroscience. However, the larger degree of similarity between the brain images was the major challenge in the diagnosis. The Deep Learning technique has gained excellent results on image classification among the present researches. Hence it is utilized for the classification of brain images among Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCL), Mild Cognitive Impairment (MCL), Late Mild Cognitive Impairment (LMCI), Alzheimer’s Disease (AD) which are the five classes of AD thus ensuring very precise and accurate diagnosis. The transfer learning approach has been taken up for the classification process by which three pre-trained networks, namely AlexNet, ResNet-18 and, GoogLe Net are modified and trained for 3000 images. All the three networks are trained for the same set of images which were acquired from the ADNI database.