24 May 2018 Application of stacked sparse autoencoder in automated detection of glaucoma in fundus images
Author Affiliations +
Abstract
In this contribution, intelligent identification of glaucoma from digital fundus images using stacked sparse autoencoder (SSAE) is proposed. The fundus images are initially converted to gray-scale and normalized w.r.t., background illuminance while maintaining contrast constancy across the dataset. Unfolded feature vectors from the pre-processed with proper rescaling and grays-scale converted fundus images are fed to SSAE for learning efficient feature representation and classification thereof using a softmax layer. A comparative evaluation highlighting the superiority of SSAE method with existing state-of the art techniques is presented to validate its efficacy in glaucoma detection. The proposed framework can be used as a clinical decision support system assisting ophthalmologists in confirming their diagnosis with high reliability and accuracy.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sawon Pratiher, Subhankar Chattoraj, Karan Vishwakarma, "Application of stacked sparse autoencoder in automated detection of glaucoma in fundus images", Proc. SPIE 10677, Unconventional Optical Imaging, 106772X (24 May 2018); doi: 10.1117/12.2291992; https://doi.org/10.1117/12.2291992
PROCEEDINGS
4 PAGES


SHARE
Back to Top