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作者:

Pan, Hongyi (Pan, Hongyi.) | Miao, Jingpeng (Miao, Jingpeng.) | Yu, Jie (Yu, Jie.) | Dong, Jingran (Dong, Jingran.) | Zhang, Mingming (Zhang, Mingming.) | Wang, Xiaobing (Wang, Xiaobing.) | Feng, Jihong (Feng, Jihong.)

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摘要:

Retinal diseases such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness without timely diagnosis and treatment. Optical coherence tomography (OCT) has been widely utilized to detect retinal diseases because of its non-contact and non-invasive imaging peculiarities. Due to the lack of ophthalmic medical resources, automatic analyzing and diagnosing retinal OCT images is necessary with computer-aided diagnosis algorithms. In this study, we propose a lightweight retinal OCT image classification model integrating convolutional neural network (CNN) and Transformer to classify various retinal diseases with few parameters of the model. Local lesion features extracted by CNN can be encoded with the whole OCT image through the Transformer, which improves the classification ability. A convolutional block attention module is also integrated into our model to enhance the representational power. Compared with several classical models, our model achieves the best accuracy of 0.9800 and recall of 0.9799 with the least number of parameters and prediction time for an image on the OCT-C8 dataset. Moreover, on the OCT2017 dataset, our model outperforms the four state-of-the-art models except almost equal to another, achieving an average accuracy, precision, recall, specificity and F1-score of 0.9985, 0.9970, 0.9970, 0.9990, and 0.9970. Simultaneously, the number of parameters of our model has been reduced to just 1.28 M, and the average prediction time for an image is only 2.5 ms. © 2024 Elsevier Ltd

关键词:

Ophthalmology Optical coherence tomography Convolutional neural networks

作者机构:

  • [ 1 ] [Pan, Hongyi]Department of Biomedical Engineering, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Miao, Jingpeng]Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing; 100730, China
  • [ 3 ] [Yu, Jie]Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing; 100730, China
  • [ 4 ] [Dong, Jingran]Department of Biomedical Engineering, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Zhang, Mingming]Sports and Medicine Integrative Innovation Center, Capital University of Physical Education and Sports, Beijing100191, China
  • [ 6 ] [Wang, Xiaobing]Sports and Medicine Integrative Innovation Center, Capital University of Physical Education and Sports, Beijing100191, China
  • [ 7 ] [Wang, Xiaobing]Department of Ophthalmology, Beijing Boai Hospital, China Rehabilitation Research Center, School of Rehabilitation Medicine, Capital Medical University, Beijing; 100068, China
  • [ 8 ] [Feng, Jihong]Department of Biomedical Engineering, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing; 100124, China

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来源 :

Biomedical Signal Processing and Control

ISSN: 1746-8094

年份: 2025

卷: 101

5 . 1 0 0

JCR@2022

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