Laser & Optoelectronics Progress, Volume. 58, Issue 1, 117002(2021)
Transfer Learning-Based Classification of Optical Coherence Tomography Retinal Images
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Fig. 2. OCT images of diabetic retinopathy. (a) Choroidal neovascularization; (b) diabetic macular edema; (c) drusen; (d) normal retina
Fig. 5. Training set curves of control experimental networks. (a)(d) Training set curves of direct transfer learning group; (b)(e) training set curves of fine-tuning transfer learning group; (c)(f) training set curves of GAP transfer learning group
Fig. 6. ROC curve of GAP transfer learning group network classification result. (a) InceptionV3 network; (b) VGG19 network; (c) ResNet50 network
Table 1. Retina data set partition
View tableTable 1. Retina data set partition
Category Number of images in data set Training Validation Test All CNV 32477 3820 1912 38209 DME 9647 1136 567 11350 Drusen 7323 862 431 8616 Normal 22372 2632 1316 26320 All 71819 8450 4226 84495
Table 2. Test accuracy of different pre-training models in control experiment
View tableTable 2. Test accuracy of different pre-training models in control experiment
Group Test accuracy/% InceptionV3 VGG19 ResNet50 Fine-tuning transfer learning group 94.9 94.8 92.9 GAP transfer learning group 97.3 96.9 94.4
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Lian Chaoming, Zhong Shuncong, Zhang Tianfu, Zhou Ning, Xie Maosong. Transfer Learning-Based Classification of Optical Coherence Tomography Retinal Images[J]. Laser & Optoelectronics Progress, 2021, 58(1): 117002
Paper Information
Category: Medical Optics and Biotechnology
Received: May. 9, 2020
Accepted: --
Published Online: Jan. 4, 2021
The Author Email: Zhong Shuncong (zhongshuncong@hotmail.com)
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