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 Alzheimer’s disease which can extract low-to-high-level features. Classification of clinical data for Alzheimer’s disease has always been challenging as there is no clinical test for Alzheimer’s disease. Doctors diagnose it by conducting assessments of patient’s 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 Alzheimer’s disease or not. The accurate diagnosis of Alzheimer’s 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 Alzheimer’s disease detection.
A.J. Dinu, R. Ganesan, Felix Joseph and V. Balaji, 2017. Quality Analysis of Various Deep Learning Neural Network Classifiers for Alzheimer’s Disease Detection. Journal of Engineering and Applied Sciences, 12: 8334-8339.