ANALYSIS OF TRANSFER LEARNING FOR SELECT RETINAL DISEASE CLASSIFICATION.

ABSTRACT:

Purpose

To analyze the effect of transfer learning for classification of diabetic retinopathy (DR) by fundus photography and select retinal diseases by spectral domain optical coherence tomography (SD-OCT).

Methods

Five widely used open-source deep neural networks and four customized simpler and smaller networks, termed the CBR family, were trained and evaluated on two tasks: 1) classification of DR using fundus photography and 2) classification of drusen, choroidal neovascularization, and diabetic macular edema using SD-OCT. For DR classification, the quadratic weighted Kappa coefficient was used to measure the level of agreement between each network and ground truth-labeled test cases. For SD-OCT-based classification, accuracy was calculated for each network. Kappa and acc

SUBMITTER: Gelman R 

PROVIDER: S-EPMC8702452 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature