Retinal disease projection conditioning by biological traits

Muhammad Hassan, Hao Zhang, Ahmed Ameen Fateh, Shuyue Ma, Wen Liang, Dingqi Shang, Jiaming Deng, Ziheng Zhang, Tsz Kwan Lam, Ming Xu, Qiming Huang, Dongmei Yu, Canyang Zhang, Zhou You, Wei Pang, Chengming Yang, Peiwu Qin

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Fundus image captures rear of an eye which has been studied for disease 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. 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 trait’s association, we embed 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, which correspondingly estimates 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. In this study, we analyzed fundus images and their corresponding association in terms of aging and gender. Our proposed models outperform randomly selected state-of-the-art DL models.
Original languageEnglish
Pages (from-to)257-271
Number of pages15
JournalComplex and Intelligent Systems
Issue number1
Early online date19 Jul 2023
Publication statusPublished - Feb 2024


  • Age
  • Aging effects
  • Biological traits
  • FAG-Net
  • FGC-Net
  • Fundus images
  • GAN
  • Gender

ASJC Scopus subject areas

  • Information Systems
  • Engineering (miscellaneous)
  • Computational Mathematics
  • Artificial Intelligence


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