An Intelligent Alzheimer’s Disease Prediction Using Convolutional Neural Network (CNN)
Year: 2020
Type: preprint
Abstract: Deep Learning is a subset of machine learning, designed to continually analyze data with logic similar to human. It uses a layered structure of an algorithm called Artificial Neural Network (ANN). They are mainly used in medical diagnosis for making critical decisions like disease prediction, robotic surgery, and radiation treatments. Disease prediction includes identifying and classifying Alzheimer's disease. It is the most common cause of dementia which affects around 46 million people in the world. The disease has several stages and it is classified into Mild and Severe. The symptoms include reduced ability to remember the information, impaired speaking and writing. Many machine learning algorithm techniques like Decision tree classifier, Independent Component Analysis, Linear Discriminant Analysis (LDA) were used to predict the disease based on their stages, but the precision in identifying stages of the signals is not much good. In this work, a Deep Learning based technique is proposed which improves the accuracy of classification by using the Convolutional Neural Network (CNN). This work analyzes the Electroencephalogram (EEG) signal, extracts the features using Fast Fourier Transform(FFT) and classifies the disease by CNN.
Source: SSRN Electronic Journal
Authors: L.Dharshana Deepthi, D. Shanthi, M. Buvana
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3176700872