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http://dx.doi.org/10.3837/tiis.2020.09.001

A Parallel Deep Convolutional Neural Network for Alzheimer's disease classification on PET/CT brain images  

Baydargil, Husnu Baris (Department of Electric Electronic and Communication Engineering, Kyungsung University)
Park, Jangsik (Department of Electric Electronic and Communication Engineering, Kyungsung University)
Kang, Do-Young (Department of Nuclear Medicine, Dong-a University College of Medicine, Dong-A University Hospital)
Kang, Hyun (Institute of Convergence Bio-Health, Dong-A University)
Cho, Kook (College of General Education, Dong-A University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.9, 2020 , pp. 3583-3597 More about this Journal
Abstract
In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.
Keywords
Computer vision; Deep learning; Convolutional neural networks; Parallel model; Image classification; Alzheimer's disease;
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