HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Health Sciences / Faculty of Health Sciences >
Peer-reviewed Journal Articles, etc >

Evaluating the Overall Accuracy of Additional Learning and Automatic Classification System for CT Images

This item is licensed under:Creative Commons Attribution 4.0 International

Files in This Item:
applsci-09-00682.pdf1.78 MBPDFView/Open
Please use this identifier to cite or link to this item:

Title: Evaluating the Overall Accuracy of Additional Learning and Automatic Classification System for CT Images
Authors: Sugimori, Hiroyuki Browse this author →KAKEN DB
Keywords: deep learning
medical image classification
additional learning
CT image
automatic training
Issue Date: 17-Feb-2019
Publisher: MDPI
Journal Title: Applied sciences
Volume: 9
Issue: 4
Start Page: 682
Publisher DOI: 10.3390/app9040682
Abstract: A large number of images that are usually registered images in a training dataset are required for creating classification models because training of images using a convolutional neural network is done using supervised learning. It takes a significant amount of time and effort to create a registered dataset because recently computed tomography (CT) and magnetic resonance imaging devices produce hundreds of images per examination. This study aims to evaluate the overall accuracy of the additional learning and automatic classification systems for CT images. The study involved 700 patients, who were subjected to contrast or non-contrast CT examination of brain, neck, chest, abdomen, or pelvis. The images were divided into 500 images per class. The 10-class dataset was prepared with 10 datasets including with 5000-50,000 images. The overall accuracy was calculated using a confusion matrix for evaluating the created models. The highest overall reference accuracy was 0.9033 when the model was trained with a dataset containing 50,000 images. The additional learning for manual training was effective when datasets with a large number of images were used. The additional learning for automatic training requires models with an inherent higher accuracy for the classification.
Rights: © 2019 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (
Type: article
Appears in Collections:保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 杉森 博行

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University