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Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection
Article

Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function

by 1,‡, 1,‡, 2,‡, 3,4,‡ and 1,*
1
College of Information Science and Engineering, Ritsumeikan University, Shiga 603-8577, Japan
2
College of General Education, Dong-A University, Busan 49315, Korea
3
Institute of Convergence Bio-Health, Dong-A University, Busan 49201, Korea
4
Department of Nuclear Medicine, Dong-A University College of Medicine, Busan 49201, Korea
*
Author to whom correspondence should be addressed.
This paper is an extended version of paper published in the 7th International Conference on Innovation in Medicine and Healthcare held in St. Julians, Malta, 17–19 June 2019.
These authors contribute equally to this work.
Appl. Sci. 2020, 10(3), 965; https://doi.org/10.3390/app10030965
Received: 17 October 2019 / Revised: 11 December 2019 / Accepted: 13 December 2019 / Published: 2 February 2020
(This article belongs to the Special Issue Computer-aided Biomedical Imaging 2020: Advances and Prospects)
Alzheimer’s disease (AD) is an irreversible progressive cerebral disease with most of its symptoms appearing after 60 years of age. Alzheimer’s disease has been largely attributed to accumulation of amyloid beta (Aβ), but a complete cure has remained elusive. 18F-Florbetaben amyloid positron emission tomography (PET) has been shown as a more powerful tool for understanding AD-related brain changes than magnetic resonance imaging and computed tomography. In this paper, we propose an accurate classification method for scoring brain amyloid plaque load (BAPL) based on deep convolutional neural networks. A joint discriminative loss function was formulated by adding a discriminative intra-loss function to the conventional (cross-entropy) loss function. The performance of the proposed joint loss function was compared with that of the conventional loss function in three state-of-the-art deep neural network architectures. The intra-loss function significantly improved the BAPL classification performance. In addition, we showed that the mix-up data augmentation method, originally proposed for natural image classification, was also useful for medical image classification. View Full-Text
Keywords: Alzheimer’s disease; deep learning; convolutional neural network; PET image; brain amyloid plaque load (BAPL) score; coronal plane; intra-loss function; joint loss function; mix-up; data augmentation Alzheimer’s disease; deep learning; convolutional neural network; PET image; brain amyloid plaque load (BAPL) score; coronal plane; intra-loss function; joint loss function; mix-up; data augmentation
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MDPI and ACS Style

Sato, R.; Iwamoto, Y.; Cho, K.; Kang, D.-Y.; Chen, Y.-W. Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function . Appl. Sci. 2020, 10, 965. https://doi.org/10.3390/app10030965

AMA Style

Sato R, Iwamoto Y, Cho K, Kang D-Y, Chen Y-W. Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function . Applied Sciences. 2020; 10(3):965. https://doi.org/10.3390/app10030965

Chicago/Turabian Style

Sato, Ryosuke, Yutaro Iwamoto, Kook Cho, Do-Young Kang, and Yen-Wei Chen. 2020. "Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function " Applied Sciences 10, no. 3: 965. https://doi.org/10.3390/app10030965

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