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作者:

Imran, Azhar (Imran, Azhar.) | Li, Jianqiang (Li, Jianqiang.) | Pei, Yan (Pei, Yan.) | Akhtar, Faheem (Akhtar, Faheem.) | Yang, Ji-Jiang (Yang, Ji-Jiang.) | Dang, Yanping (Dang, Yanping.)

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摘要:

Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词:

Image classification Salvaging Learning systems Classification (of information) Extraction Ophthalmology Deep learning Convolution Feature extraction Support vector machines Convolutional neural networks Eye protection

作者机构:

  • [ 1 ] [Imran, Azhar]School of Software Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Li, Jianqiang]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Pei, Yan]Computer Science Division, University of Aizu, Fukushima, Japan
  • [ 4 ] [Akhtar, Faheem]School of Software Engineering, Beijing University of Technology, Beijing, China
  • [ 5 ] [Akhtar, Faheem]Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan
  • [ 6 ] [Yang, Ji-Jiang]Research Institute of Information Technology, Tsinghua University, Beijing, China
  • [ 7 ] [Dang, Yanping]General Internal Medicine, Beijing Moslem Hospital, Beijing, China

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来源 :

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

ISSN: 2168-1163

年份: 2020

期: 6

卷: 8

页码: 691-698

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