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- Title
Evaluating the Overall Accuracy of Additional Learning and Automatic Classification System for CT Images.
- Authors
Sugimori, Hiroyuki
- Abstract
Featured Application: This article describes the evaluation of the automatic classification system for computed tomography (CT) images using a deep learning technique. Additional learning for automatic training will help to create various classification models in the medical fields. The results in this study will be useful for creating new classification models. 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.
- Subjects
AUTOMATIC classification; EMISSION tomography equipment; MAGNETIC resonance imaging; SUPERVISED learning
- Publication
Applied Sciences (2076-3417), 2019, Vol 9, Issue 4, p682
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app9040682