Classification of large-scale image database of various skin diseases using deep learning

Masaya Tanaka, Atsushi Saito, Kosuke Shido, Yasuhiro Fujisawa, Kenshi Yamasaki, Manabu Fujimoto, Kohei Murao, Youichirou Ninomiya, Shin’ichi Satoh, Akinobu Shimizu

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions. Methods: ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists. Patient-wise classification is performed by aggregating the inference vectors of all the input patient images. Results: The experiment using 70,196 images of 13,038 patients demonstrated that classification accuracy was significantly improved by both metric learning and aggregation, resulting in patient accuracies of 0.579 for Top-1, 0.793 for Top-3, and 0.863 for Top-5. The McNemar test showed that the improvements achieved by the proposed method were statistically significant. Conclusion: This study presents a deep learning-based classification of 59 skin diseases using multiple photographic images of a patient. The experimental results demonstrated that the proposed classification reinforced by metric learning and aggregation of multiple input images was effective in the classification of patients with diverse skin diseases and imaging conditions.

Original languageEnglish
JournalInternational Journal of Computer Assisted Radiology and Surgery
DOIs
Publication statusAccepted/In press - 2021
Externally publishedYes

Keywords

  • Computer-aided diagnosis
  • Diffuse skin disease
  • Localized skin disease
  • Photographic image

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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