TY - GEN
T1 - Detection of age-related macular degeneration via deep learning
AU - Burlina, P.
AU - Freund, D. E.
AU - Joshi, N.
AU - Wolfson, Y.
AU - Bressler, N. M.
N1 - Funding Information:
The support of NIH grant R21 EY024310 is gratefully acknowledged. The views expressed herein are only those of the authors and are not mean to reflect in any way the views of NIH or NEI
Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - Age-related macular generation (AMD) - when left untreated - is the main cause of blindness for individuals over the age of 50. With the US population now counting over 100 million individuals over 50, it is imperative to develop methods that can effectively determine which individuals with an earlier, often asymptomatic stage, are at risk of developing the advanced stage that can cause severe vision loss. This paper studies the appropriateness of the transfer of image features computed from pre-trained deep neural networks to the problem in AMD detection. Tests using over 5600 images from the NIH AREDS dataset (the largest dataset used thus far for AMD image analysis studies) show good preliminary results (between nearly 92% and 95% accuracy).
AB - Age-related macular generation (AMD) - when left untreated - is the main cause of blindness for individuals over the age of 50. With the US population now counting over 100 million individuals over 50, it is imperative to develop methods that can effectively determine which individuals with an earlier, often asymptomatic stage, are at risk of developing the advanced stage that can cause severe vision loss. This paper studies the appropriateness of the transfer of image features computed from pre-trained deep neural networks to the problem in AMD detection. Tests using over 5600 images from the NIH AREDS dataset (the largest dataset used thus far for AMD image analysis studies) show good preliminary results (between nearly 92% and 95% accuracy).
KW - Age-related macular degeneration
KW - deep learning
KW - pre-trained networks
UR - http://www.scopus.com/inward/record.url?scp=84978427918&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978427918&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493240
DO - 10.1109/ISBI.2016.7493240
M3 - Conference contribution
AN - SCOPUS:84978427918
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 184
EP - 188
BT - 2016 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Y2 - 13 April 2016 through 16 April 2016
ER -