Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning

BMC Bioinformatics

Jinn-Tsong Tsai¹  Wen-Hsien Ho²  Wei-Tai Huang³  Yao-Mei Chen⁴ 

[1] Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, 807, Kaohsiung, Taiwan;Department of Computer Science, National Pingtung University, 900, Pingtung, Taiwan;Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, 807, Kaohsiung, Taiwan;Department of Medical Research, Kaohsiung Medical University Hospital, 807, Kaohsiung, Taiwan;Department of Mechanical Engineering, National Pingtung University of Science and Technology, 912, Pingtung, Taiwan;School of Nursing, Kaohsiung Medical University, 807, Kaohsiung, Taiwan;Superintendent Office, Kaohsiung Medical University Hospital, 807, Kaohsiung, Taiwan;

关键词: Convolutional neural network;    Transfer learning;    Hyperparameter;    Optical coherence tomography image;    Age-related macular degeneration;   

DOI  :  10.1186/s12859-021-04001-1

来源: Springer

【 摘 要 】

BackgroundTo diagnose key pathologies of age-related macular degeneration (AMD) and diabetic macular edema (DME) quickly and accurately, researchers attempted to develop effective artificial intelligence methods by using medical images.ResultsA convolutional neural network (CNN) with transfer learning capability is proposed and appropriate hyperparameters are selected for classifying optical coherence tomography (OCT) images of AMD and DME. To perform transfer learning, a pre-trained CNN model is used as the starting point for a new CNN model for solving related problems. The hyperparameters (parameters that have set values before the learning process begins) in this study were algorithm hyperparameters that affect learning speed and quality. During training, different CNN-based models require different algorithm hyperparameters (e.g., optimizer, learning rate, and mini-batch size). Experiments showed that, after transfer learning, the CNN models (8-layer Alexnet, 22-layer Googlenet, 16-layer VGG, 19-layer VGG, 18-layer Resnet, 50-layer Resnet, and a 101-layer Resnet) successfully classified OCT images of AMD and DME.ConclusionsThe experimental results further showed that, after transfer learning, the VGG19, Resnet101, and Resnet50 models with appropriate algorithm hyperparameters had excellent capability and performance in classifying OCT images of AMD and DME.

【 授权许可】

CC BY