An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.

ABSTRACT:

Purpose

To develop an intelligent grading model for myopic maculopathy based on a long-tail learning framework, using the improved loss function LTBSoftmax. The model addresses the long-tail distribution problem in myopic maculopathy data to provide preliminary grading, aiming to improve grading capability and efficiency.

Methods

This study includes a data set of 7529 color fundus photographs. Experienced ophthalmologists meticulously annotated the ground truth. A new intelligent grading model for myopic maculopathy was constructed using the improved loss function LTBSoftmax, which predicts lesions by locally enhancing feature extraction with ND Block. Standard grading metrics were selected to evaluate the LTBSoftmax model.

Results

The improved model demonstrated exce

SUBMITTER: Zheng B 

PROVIDER: S-EPMC11895850 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature