Chrome Extension
WeChat Mini Program
Use on ChatGLM

Jointly Using Deep Model Learned Features and Traditional Visual Features in a Stacked SVM for Medical Subfigure Classification.

Sino-foreign-interchange Workshop on Intelligent Science and Intelligent Data Engineering(2017)

Cited 1|Views15
Abstract
Classification of diagnose images and illustrations in the literature is a major challenge towards automated literature review and retrieval. Although being widely recognized as the most successful image classification technique, deep learning models, however, may need to be complemented by traditional visual features to solve this problem, in which there are intra-class variation, inter-class similarity and a small training dataset. In this paper, we propose an approach to classifying diagnose images and biomedical publication illustrations. This algorithm jointly uses the image representations learned by three pre-trained deep convolutional neural network models and ten types of traditional visual features in a stacked support vector machine (SVM) classifier. We have evaluated this algorithm on the ImageCLEF 2016 Subfigure Classification dataset and achieved an accuracy of 85.62%, which is higher than the top performance of purely visual approaches in this challenge.
More
Translated text
Key words
Medical image classification,Feature extraction,Deep convolutional neural network,Stacked support vector machine
求助PDF
上传PDF
Bibtex
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined