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Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer's disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.

Alzheimer's Disease (AD) is the most common form of dementia that can lead to a neurological brain disorder that causes progressive memory loss as a result of damaging the brain cells and the ability to perform daily activities. This disease is one of kind and fatal. Early detection of AD because of its progressive threat and patients all around the world. The early detection is promising as it can help to predetermine the condition of lot of patients they might face in the future. So, by examining the consequences of the disease, using MRI images we can get the help of Artificial intelligence (AI) technology to classify the AD patients if they have or may not have the deadly disease in future. In recent years, AI-based Machine Learning (ML) techniques are very useful for the diagnosis of AD. In this paper, we have applied different machine learning techniques such as Logistic Regression, Decision Tree, Random forest classifier, Support Vector Machine and AdaBoost for the earlier diagnosis and classification of Alzheimer 's disease using Open Access Series of Imaging Studies (OASIS) dataset, in which a significant performance and result gained on classification with Random Forest classifier.

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