• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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


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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Cite this article

[IEEE Style]
H. B. Baydargil, J. Park, D. Kang, H. Kang and K. Cho, "A Parallel Deep Convolutional Neural Network for Alzheimer’s disease classification on PET/CT brain images," KSII Transactions on Internet and Information Systems, vol. 14, no. 9, pp. 3583-3597, 2020. DOI: 10.3837/tiis.2020.09.001.

[ACM Style]
Husnu Baris Baydargil, Jangsik Park, Do-Young Kang, Hyun Kang, and Kook Cho. 2020. A Parallel Deep Convolutional Neural Network for Alzheimer’s disease classification on PET/CT brain images. KSII Transactions on Internet and Information Systems, 14, 9, (2020), 3583-3597. DOI: 10.3837/tiis.2020.09.001.