Classification of Alzheimer's Disease with Stacked Convolutional Autoencoder

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The inpu...

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Bibliographic Details
Published in멀티미디어학회논문지 Vol. 23; no. 2; pp. 216 - 226
Main Authors Baydargil, Husnu Baris, Park, Jang Sik, Kang, Do Young
Format Journal Article
LanguageKorean
Published 2020
Subjects
Image Classification
Alzheimer's Disease
Medical Imaging
Convolutional Neural Networks
Computer Vision
Deep Learning
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Summary:In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.
Bibliography:KISTI1.1003/JNL.JAKO202010163508821
ISSN:1229-7771
2384-0102