Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning.
BMC Bioinformatics
; 22(Suppl 5): 99, 2021 Nov 08.
Article
en En
| MEDLINE
| ID: mdl-34749641
ABSTRACT
BACKGROUND:
To 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.RESULTS:
A 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.CONCLUSIONS:
The 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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Edema Macular
/
Retinopatía Diabética
/
Degeneración Macular
Límite:
Humans
Idioma:
En
Año:
2021
Tipo del documento:
Article