Study on discrimination of Alzheimer's disease states using an ensemble neural network's model

Junsik Eom, Hanbyol Jang, Sewon Kim, Jinseong Jang, Dosik Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Alzheimer's Disease (AD) is an irreversible disease that gradually worsens with time. Therefore, early diagnosis of Alzheimer's disease is important to prevent brain tissue damage and treat the patient properly. Mild Cognitive Impairment (MCI) is a prodromal stage of AD, which has no harm to the patient's ability to have functional activities in daily life except a minor cognitive deficiency. Since MCI can be detected at the earliest stage of AD, it is critical to detect patients with MCI to delay the progression of AD. It is possible to distinguish patients with AD, MCI, and Normal Control (NC) from one another by the size of brain volume, hippocampus and patient's clinical information. The brain and hippocampus gradually shrink in size and shape as AD develops. In this study, we propose a deep learning-based technique to classify patients with AD, MCI and NC by brain Magnetic Resonance (MR) images. Deep learning has shown human-level performance in a lot of studies including medical image analysis with constrained amount of training data. We propose a deep learning-based ensemble model which consists of 3 Convolutional Neural Networks (CNN) [1] with Network In Network (NIN) [2] architecture. The kernel size is 3x3 convolution followed by 1x1 convolution to reduce the number of trainable parameters and extract features for classification better. In addition, Global Averaging Pooling (GAP) is used instead of Fully-Connected (FC) layers to avoid overfitting by reducing the number of trainable parameters. By using the ensemble model, this shows the 81.66% in classifying 3 classes.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
PublisherSPIE
ISBN (Electronic)9781510625471
DOIs
Publication statusPublished - 2019 Jan 1
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: 2019 Feb 172019 Feb 20

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10950
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego
Period19/2/1719/2/20

Fingerprint

Neural Networks (Computer)
discrimination
Alzheimer Disease
Neural networks
impairment
brain
Brain
hippocampus
learning
Learning
Convolution
convolution integrals
Hippocampus
Patient Harm
Prodromal Symptoms
Aptitude
Discrimination (Psychology)
Magnetic resonance
classifying
image analysis

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Eom, J., Jang, H., Kim, S., Jang, J., & Hwang, D. (2019). Study on discrimination of Alzheimer's disease states using an ensemble neural network's model. In K. Mori, & H. K. Hahn (Eds.), Medical Imaging 2019: Computer-Aided Diagnosis [1095029] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10950). SPIE. https://doi.org/10.1117/12.2512732
Eom, Junsik ; Jang, Hanbyol ; Kim, Sewon ; Jang, Jinseong ; Hwang, Dosik. / Study on discrimination of Alzheimer's disease states using an ensemble neural network's model. Medical Imaging 2019: Computer-Aided Diagnosis. editor / Kensaku Mori ; Horst K. Hahn. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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abstract = "Alzheimer's Disease (AD) is an irreversible disease that gradually worsens with time. Therefore, early diagnosis of Alzheimer's disease is important to prevent brain tissue damage and treat the patient properly. Mild Cognitive Impairment (MCI) is a prodromal stage of AD, which has no harm to the patient's ability to have functional activities in daily life except a minor cognitive deficiency. Since MCI can be detected at the earliest stage of AD, it is critical to detect patients with MCI to delay the progression of AD. It is possible to distinguish patients with AD, MCI, and Normal Control (NC) from one another by the size of brain volume, hippocampus and patient's clinical information. The brain and hippocampus gradually shrink in size and shape as AD develops. In this study, we propose a deep learning-based technique to classify patients with AD, MCI and NC by brain Magnetic Resonance (MR) images. Deep learning has shown human-level performance in a lot of studies including medical image analysis with constrained amount of training data. We propose a deep learning-based ensemble model which consists of 3 Convolutional Neural Networks (CNN) [1] with Network In Network (NIN) [2] architecture. The kernel size is 3x3 convolution followed by 1x1 convolution to reduce the number of trainable parameters and extract features for classification better. In addition, Global Averaging Pooling (GAP) is used instead of Fully-Connected (FC) layers to avoid overfitting by reducing the number of trainable parameters. By using the ensemble model, this shows the 81.66{\%} in classifying 3 classes.",
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Eom, J, Jang, H, Kim, S, Jang, J & Hwang, D 2019, Study on discrimination of Alzheimer's disease states using an ensemble neural network's model. in K Mori & HK Hahn (eds), Medical Imaging 2019: Computer-Aided Diagnosis., 1095029, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10950, SPIE, Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, United States, 19/2/17. https://doi.org/10.1117/12.2512732

Study on discrimination of Alzheimer's disease states using an ensemble neural network's model. / Eom, Junsik; Jang, Hanbyol; Kim, Sewon; Jang, Jinseong; Hwang, Dosik.

Medical Imaging 2019: Computer-Aided Diagnosis. ed. / Kensaku Mori; Horst K. Hahn. SPIE, 2019. 1095029 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10950).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Eom J, Jang H, Kim S, Jang J, Hwang D. Study on discrimination of Alzheimer's disease states using an ensemble neural network's model. In Mori K, Hahn HK, editors, Medical Imaging 2019: Computer-Aided Diagnosis. SPIE. 2019. 1095029. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512732