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VGG-based BAPL Score Classification of 18F-Florbetaben Amyloid Brain PET
Biomed Sci Letters 2018;24:418-425
Published online December 31, 2018;
© 2018 The Korean Society For Biomedical Laboratory Sciences.

Hyeon Kang1,*, Woong-Gon Kim3,**, Gyung-Seung Yang5,**, Hyun-Woo Kim4,**,
Ji-Eun Jeong1,2,**, Hyun-Jin Yoon1,2,**, Kook Cho1,**, Young-Jin Jeong1,2,*** and Do-Young Kang1,2,†,***

1Institute of Convergence Bio-Health, Dong-A University, Busan 49201, Korea
2Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, Busan 49201, Korea
3Economic Survey, Gyeongin Regional Statistics Office, Gwacheon 13809, Korea
4Department of Industrial Engineering, Hanyang University, Seoul 04763, Korea
5Ubicod Company, Seoul 08381, Korea
Correspondence to: *Graduate student, **Researcher, ***Professor.
Do-Young Kang. Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, #26 Daesingongwon-ro, Seo-gu, Busan, 49201, Korea.
Tel: +82-51-240-5630, Fax: +82-51-242-7237, e-mail:
Received November 26, 2018; Accepted December 6, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the β-Amyloid (Aβ) deposition. We designed a convolutional neural network (CNN) model that predicts the Aβ-positive and Aβ-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the Aβ-positive and Aβ-negative status.
Keywords : Alzheimer's disease, β-Amyloid, Convolutional neural network, 18F-florbetaben PET, Gray matter