CLASSIFICATION OF DEMENTIA DISEASES USING DEEP LEARNING TECHNIQUES

Abstract

Dementia is the most frequent degenerative sickness in adults where early diagnosis can forestall or prolong progression. In this study, we used a deep learning techniques for classification of dementia. Data were collected from OASIS database of all the patients receiving dementia screening. The data included the patient’s sex, age, education, social economic status, Mini-Mental State Examination, Clinical Dementia Rating, Atlas Scaling Factor, Estimated Total Intracranial Volume and Normalized Whole Brain Volume. The performance of every algorithm is juxtaposed with Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBNN), Multilayer Perceptron Neural Network (MPNN) and Long Short Term Memory (LSTM) using Sensitivity, Specificity, Detection Rate. The results show that with 100% efficiency, GRNN, RBNN and LSTM tend to be the best in the classification of dementia. The use of deep learning such as LSTM for early diagnosis of dementia can help improve the process of dementia diagnosis.