Alzheimer disease is the most prevailing type in dementia that causes permanent damage in the neuron connections and builds up dead cells results in behavioral changes, memory loss, and cognitive decline. So, an early detection of Alzheimer helps to manage the disease progression and helps to slow down rate of progression. The CNN model has been developed with an augmented data by setting the classification task with pre-processed images based on magnetic resonance imaging. The trained CNN model shows the capability to be a stage-wise classifier of the MRI scanned images into categories such as very_mild_demented, mild_demented, and moderate_demented besides the non_demented category. Various optimizers such as SGD, Adam, RMS-Prop as well as Adagrad are analyzed and studied. Performance of each fine-tuned model is evaluated by calculating Precision, Recall, F1-score, and Accuracy. Moreover, our proposed CNN Model has achieved accuracy of 99.69%, outperformed the existing models.