Open AccessEarly Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture
Author(s) -
Deepthi Kamath,
Misba Firdose Fathima,
Monica K. P,
Kusuma Mohanchandra
Publication year - 2021
Publication title -
international journal of artificial intelligence (batam)/international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2686-3251
pISSN - 2407-7275
DOI - 10.36079/lamintang.ijai-0802.232
Subject(s) - computer science , convolutional neural network , dementia , artificial intelligence , classifier (uml) , artificial neural network , alzheimer's disease , pattern recognition (psychology) , disease , machine learning , neuroscience , medicine , pathology , psychology
Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.