Comparison of medical image classification accuracy among three machine learning methods.

Journal: Journal of X-ray science and technology

Published Date: Jan 1, 2018

BACKGROUND: Low-quality medical images may influence the accuracy of the machine learning process.

Tomoko Maruyama

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Norio Hayashi

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Yusuke Sato

Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Shingo Hyuga

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Yuta Wakayama

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Haruyuki Watanabe

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Akio Ogura

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
Toshihiro Ogura

Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.

Databases, Factual Deep Learning Diagnostic Imaging Humans Image Processing, Computer-Assisted Machine Learning Neural Networks, Computer Support Vector Machine

View on PubMed Access via DOI PubMed (30223423)