Comparison of medical image classification accuracy among three machine learning methods

Article type: Research Article

Authors: Maruyama, Tomoko^{a; *} | Hayashi, Norio^a | Sato, Yusuke^b | Hyuga, Shingo^a | Wakayama, Yuta^a | Watanabe, Haruyuki^a | Ogura, Akio^a | Ogura, Toshihiro^a

Affiliations: [a] Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan | [b] Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan

Correspondence: [*] Corresponding author: Tomoko Maruyama, Department of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamioki, Maebashi, Gunma, 371-0052, Japan. Tel.: +81 27 235 1211; Fax: +81 27 235 2501; E-mail: [email protected].

Abstract: BACKGROUND:Low-quality medical images may influence the accuracy of the machine learning process. OBJECTIVE:This study was undertaken to compare accuracy of medical image classification among machine learning methods, as classification is a basic aspect of clinical image inspection. METHODS:Three types of machine learning methods were used, which include Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN). To investigate changes in accuracy related to image quality, we constructed a single dataset using two different file formats of DICOM (Digital Imaging and Communications in Medicine) and JPEG (Joint Photographic Experts Group). RESULTS:The JPEG format contains less color information and data capacity than the DICOM format. CNN classification was accurate for both datasets, whereas SVM and ANN accuracy decreased with the loss of data from DICOM to JPEG formats. CONCLUSIONS:CNN is more accurate than conventional machine learning methods that utilize the manual feature extraction.

Keywords: Deep learning, CNN, DICOM, JPEG

DOI: 10.3233/XST-18386

Journal: Journal of X-Ray Science and Technology, vol. 26, no. 6, pp. 885-893, 2018