Authors : A.J. Dinu, R. Ganesan, Felix Joseph and V. Balaji
Abstract: Over the past decade, deep learning has become a powerful machine learning algorithm in the classification of clinical data for human conditions such as Alzheimers disease which can extract low-to-high-level features. Classification of clinical data for Alzheimers disease has always been challenging as there is no clinical test for Alzheimers disease. Doctors diagnose it by conducting assessments of patients cognitive decline. But its particularly difficult for them to identify Mild Cognitive Impairment (MCI) at an early stage when symptoms are less obvious. Also, it is difficult to predict whether MCI patients will develop Alzheimers disease or not. The accurate diagnosis of Alzheimers disease in the early stage is important in order to take preventive measures and to reduce the progression and severity before irreversible brain damages occur. This study gives the performance of different classifiers on deep learning neural network for Alzheimers disease detection.
A.J. Dinu, R. Ganesan, Felix Joseph and V. Balaji, 2017. Quality Analysis of Various Deep Learning Neural Network Classifiers for Alzheimers Disease Detection. Journal of Engineering and Applied Sciences, 12: 8334-8339.