Application of stacked sparse autoencoder in automated detection of glaucoma in fundus images

Sawon Pratiher; Subhankar Chattoraj; Karan Vishwakarma

doi:10.1117/12.2291992

24 May 2018 Application of stacked sparse autoencoder in automated detection of glaucoma in fundus images

Sawon Pratiher, Subhankar Chattoraj, Karan Vishwakarma

Proceedings Volume 10677, Unconventional Optical Imaging; 106772X (2018) https://doi.org/10.1117/12.2291992
Event: SPIE Photonics Europe, 2018, Strasbourg, France

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.

Citation Download Citation

Sawon Pratiher, Subhankar Chattoraj, and 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); https://doi.org/10.1117/12.2291992

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