Young Researcher Paper Award 2021

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語


 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research

(proofreading and recording)

(translation service)

The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Copyright(C) MYU K.K.

Parameter Combination Optimization in Three-Dimensional Convolutional Neural Networks and Transfer Learning for Detecting Alzheimer’s Disease from Magnetic Resonance Images

Cheng-Jian Lin, Tzu-Chao Lin, and Cheng-Wei Lin

(Received March 29, 2022; Accepted May 23, 2022)

Keywords: Alzheimer’s disease, magnetic resonance imaging, three-dimensional convolutional neural networks, Taguchi experimental design, transfer learning

Alzheimer’s disease (AD) destroys neurons in the brain, engendering brain atrophy and severely compromising brain function. Magnetic resonance imaging (MRI) is widely applied to analyze brain degeneration. AD is typically detected by examining specialist-selected features of two-dimensional images or region-of-interest features identified by trained classifiers. We developed a Taguchi-based three-dimensional convolutional neural network (T-3D-CNN) model for detecting AD in magnetic resonance images. CNN parameters are generally obtained through trial-and-error methods. To stabilize the CNN diagnostic accuracy, the Taguchi method was employed for parameter combination optimization. Obtaining patient data is difficult; thus, we performed transfer learning to verify the proposed T-3D-CNN model’s effectiveness by using only a small quantity of patient data from various databases. The experimental results confirmed that the T-3D-CNN model detected AD from images in the Open Access Series of Imaging Studies (OASIS)-2 data set with 99.46% accuracy, which was 2.06 percentage points higher than that of the original 3D-CNN. After a complete investigation of the OASIS-2 data set, we selected 10, 30, 60, 80, and 100% of the data from the OASIS-1 data set to verify the T-3D-CNN and updated the original network weights through transfer learning; the average accuracies were 81.31, 92.88, 95.85, 100, and 100%, respectively.

Corresponding author: Cheng-Jian Lin

Forthcoming Regular Issues

Forthcoming Special Issues

Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 4-3
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)

Special Issue on Advanced Technologies for Remote Sensing and Geospatial Analysis: Part 2
Guest editor, Dong Ha Lee (Kangwon National University) and Myeong Hun Jeong (Chosun University)
Call for paper

Special Issue on IoT Wireless Networked Sensing for Life and Safety
Guest editor, Toshihiro Itoh (The University of Tokyo) and Jian Lu (National Institute of Advanced Industrial Science and Technology)
Call for paper

Special Issue on Biosensors and Biofuel Cells for Smart Community and Smart Life
Guest editor, Seiya Tsujimura (University of Tsukuba), Isao Shitanda (Tokyo University of Science), and Hiroaki Sakamoto (University of Fukui)
Call for paper

Special Issue on Novel Sensors and Related Technologies on IoT Applications: Part 1
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper

Special Issue on Advanced Ubiquitous Computing Systems for Society 5.0
Guest editor, Manato Fujimoto (Osaka City University)
Call for paper

Copyright(C) MYU K.K. All Rights Reserved.