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

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 CN

SUBMITTER: Chen YM 

PROVIDER: S-EPMC8576967 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature