International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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HiGANCNN: A Hybrid Generative Adversarial Network and Convolutional Neural Network for Glaucoma Detection

  • Alsulami, Fairouz (Computer Science Department, Jamoum University College, Umm Al-Qura University) ;
  • Alseleahbi, Hind (Computer Science Department, Jamoum University College, Umm Al-Qura University) ;
  • Alsaedi, Rawan (Computer Science Department, Jamoum University College, Umm Al-Qura University) ;
  • Almaghdawi, Rasha (Computer Science Department, Jamoum University College, Umm Al-Qura University) ;
  • Alafif, Tarik (Computer Science Department, Jamoum University College, Umm Al-Qura University) ;
  • Ikram, Mohammad (Computer Science Department, Jamoum University College, Umm Al-Qura University) ;
  • Zong, Weiwei (WeCare.WeTeach) ;
  • Alzahrani, Yahya (Academic Affairs and Training, Secuirty Forces Hospital) ;
  • Bawazeer, Ahmed (Department of Ophthalmology, Faculty of medicine, King Abdulaziz University)
  • 투고 : 2022.09.05
  • 발행 : 2022.09.30
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초록

Glaucoma is a chronic neuropathy that affects the optic nerve which can lead to blindness. The detection and prediction of glaucoma become possible using deep neural networks. However, the detection performance relies on the availability of a large number of data. Therefore, we propose different frameworks, including a hybrid of a generative adversarial network and a convolutional neural network to automate and increase the performance of glaucoma detection. The proposed frameworks are evaluated using five public glaucoma datasets. The framework which uses a Deconvolutional Generative Adversarial Network (DCGAN) and a DenseNet pre-trained model achieves 99.6%, 99.08%, 99.4%, 98.69%, and 92.95% of classification accuracy on RIMONE, Drishti-GS, ACRIMA, ORIGA-light, and HRF datasets respectively. Based on the experimental results and evaluation, the proposed framework closely competes with the state-of-the-art methods using the five public glaucoma datasets without requiring any manually preprocessing step.

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