Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits

TL;DR

This paper introduces novel deep learning models FAG-Net and FGC-Net for estimating age and gender from fundus images, generating image variants, and analyzing disease spread related to biological traits.

Contribution

The study presents cutting-edge deep learning models that simultaneously estimate biological traits and generate fundus image variants conditioned on age, advancing biological trait analysis in ophthalmology.

Findings

Models outperform existing state-of-the-art methods

Successfully generate fundus image variants based on age

Identify potential disease spread patterns related to age

Abstract

Fundus image captures rear of an eye, and which has been studied for the diseases identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. However, the current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the traits association, our study embeds aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. Our study analyzes fundus images and their corresponding association with biological traits, and predicts of possible spreading of ocular disease on fundus images given age as condition to the generative model. Our proposed models outperform the randomly selected state of-the-art DL models.