期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 22, 期 5, 页码 1521-1530出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2017.2775662
关键词
Medical image classification; deep convolutional neural network (DCNN); back-propagation neural network (BPNN); ensemble learning
类别
资金
- National Natural Science Foundation of China [61471297, 61771397]
- Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University [Z2017041]
- Australian Research Council Grants
The classification of medical images and illustrations from the biomedical literature is important for automated literature review, retrieval, andmining. Although deep learning is effective for large-scale image classification, it may not be the optimal choice for this task as there is only a small training dataset. We propose a combined deep and handcrafted visual feature (CDHVF) based algorithm that uses features learned by three fine-tuned and pretrained deep convolutional neural networks (DCNNs) and two handcrafted descriptors in a joint approach. We evaluated the CDHVF algorithm on the ImageCLEF 2016 Subfigure Classification dataset and it achieved an accuracy of 85.47%, which is higher than the best performance of other purely visual approaches listed in the challenge leaderboard. Our results indicate that handcrafted features complement the image representation learned by DCNNs on small training datasets and improve accuracy in certain medical image classification problems.
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