Fast Prediction of Cortical Dementia Based on Original Brain MRI images Using Convolutional Neural Network
Paper ID : 1093-MVIP2020
Seyed Morteza Amini *1, Hedieh Sajedi1, Tayeb Mahmoudi1, Sayeh Mirzaei2
1School of Mathematics, Statistics and Computer Science, Collage of Science, University of Tehran
2School of Engineering Science, College of Engineering, University of Tehran
Fast and automatic identification of different types of Cortical Dementia, specially Alzheimer’s disease, based on Brain MRI images, is a crucial technology which can help physicians in early and effective treatment. Although pre-processing of MRI images could improve the accuracy of machine learning techniques for classification of the normal and abnormal cases, this could slow down the process of automatic identification and tarnish the applicability of these methods in clinics and laboratories. In this paper we examine classification of a small sample of the original brain MRI images, using a 2D Convolutional Neural Network (CNN). The data consists of 172 healthy individuals as the control group (HC) and only 89 patients with different grades of Dementia(DP) which was collected in National Brain Mapping Center of Iran. The model could achieve an accuracy of 97.47% on the test set and 93.88% based on a 5-fold cross-validation.
Convolutional Neural Network, National Brain Mapping Center, Magnetic Resonance Imaging, Classification
Status : Paper Accepted (Poster Presentation)