Improved Classification Model using CNN for Detection of Alzheimer’s Disease

Abstract

Alzheimer’s Disease (AD) is commonly called a neurodegenerative disorder and it is a common form of dementia. There is no permanent cure for this brain disease hence the early diagnosis of such disease using medical imaging system is highly significant. Machine learning models play a vital role in the detection of AD. Since most of the conventional machine learning models find it difficult to detect the essential features to classify the disease, an advanced deep learning framework called Convolutional Neural Network (CNN) is used in this study to detect essential features automatically and classify the disease. The building components of the proposed CNN-based classification method include convolution layer, batch normalization process, ReLU, and Max-pooling operation. The main objective of this CNN-based classification method is to predict whether the patient is suffering from Alzheimer's disease through the analysis of brain MRI. The proposed methodology implemented is identical to a classification-based system that undergoes training, evaluation, and testing process. Finally, the softmax layer is applied for classification, and the Adam optimization technique is applied for reducing the loss, and by applying Adam quicker convergence can be achieved. The proposed improved CNN classification method achieves an accuracy of 97.8%.