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Classification of large-scale image database of various skin diseases using deep learning
- Source :
- International Journal of Computer Assisted Radiology and Surgery. 16:1875-1887
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- 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. 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. 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. 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.
- Subjects :
- Skin Neoplasms
Computer science
Generalization
Biomedical Engineering
Inference
Health Informatics
Overfitting
Skin Diseases
Deep Learning
McNemar's test
Photography
Humans
Radiology, Nuclear Medicine and imaging
Reliability (statistics)
business.industry
Deep learning
Reproducibility of Results
Pattern recognition
General Medicine
Computer Graphics and Computer-Aided Design
Computer Science Applications
Computer-aided diagnosis
Metric (mathematics)
Surgery
Computer Vision and Pattern Recognition
Artificial intelligence
business
Subjects
Details
- ISSN :
- 18616429 and 18616410
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- International Journal of Computer Assisted Radiology and Surgery
- Accession number :
- edsair.doi.dedup.....b426124953c1351e02d67ad48049b8c4