A Deep Learning Based Multi-model for Early prognosticate of Alzheimer’s Dementia using MRI Dataset

Alzheimer’s disease involves the death of neurons in the brain, which leads to the patient’s memory loss. It is an irreversible neurological disease which has no cure. Early prognosis is important to prevent progress of this disease and to treat the Alzheimer brain images of the patient are captured and classified based on the results. The images of the brain must be clear and it must differentiate the various parts of the brain clearly in order to find the type of the disease. suggestThis paper has suggested using a convolutional neural network to detect Alzheimer’s disease (CNN). Further, this study intends to achieve the best disease prediction by using machine learning techniquesfor the benefit of the medical team. It is one of the future scope techniques which make a machine everything possible which can be and cannot be done by humans. The Deep Learning technique has grown a lot in recent years. DL algorithms operate well with massive datasets, which is very useful in cases like AD. Also, the proposed research work has used the Convolutional Neural Network (CNN) technique to classify and identify diseases by using MRI images, with a total of 2560 normal images, 1792 MCI images, 717 early AD images, and 52 late AD portrayals.5121 photos were utilised to train the data and 1279 images were tested once the data had been trained. A significant accuracy of 94% is achieved, to cover a large dataset deep learning algorithm is more beneficial when compared with traditional techniques (machine learning).