24 May 2018 Application of stacked sparse autoencoder in automated detection of glaucoma in fundus images
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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.
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Sawon Pratiher, Sawon Pratiher, Subhankar Chattoraj, Subhankar Chattoraj, Karan Vishwakarma, 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
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