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

• Husnu Baris Baydargil,
• Jangsik Park,
• Do-Young Kang,
• Hyun Kang,
• Kook Cho,

Vol. 14, No. 9, September 30, 2020
10.3837/tiis.2020.09.001, Download Paper (Free):

• computer vision
• Deep Learning
• Convolutional neural networks
• Parallel model
• Image Classification
• Alzheimer’s disease

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%.

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