TY - GEN
T1 - Diabetic Retinopathy Grade and Macular Edema Risk Classification Using Convolutional Neural Networks
AU - He, Jia
AU - Shen, Linlin
AU - Ai, Xingfang
AU - Li, Xuechen
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Diabetic retinopathy (DR) is one of the major causes of blindness in the western world. Effective treatment of DR is available, when detected early enough, which makes this a vital process. Computers are able to obtain much quicker classifications once trained, giving the ability to aid clinicians in real-time classification. This work employed a deep convolutional neural network (CNN) based method for diabetic retinopathy classification. Three independent CNNs were employed for the classification of DR grade, macular edema risk and multi-label, which included the combination of both grade and risk classes. A fusion method was used to combine all features extracted by the CNNs and make the final classification result. The classification accuracy of the grade and risk were 0.65 and 0.72, respectively. The classification results showed the proposed network fusion method can improve the performances on both task - DR grading and macular edema risk.
AB - Diabetic retinopathy (DR) is one of the major causes of blindness in the western world. Effective treatment of DR is available, when detected early enough, which makes this a vital process. Computers are able to obtain much quicker classifications once trained, giving the ability to aid clinicians in real-time classification. This work employed a deep convolutional neural network (CNN) based method for diabetic retinopathy classification. Three independent CNNs were employed for the classification of DR grade, macular edema risk and multi-label, which included the combination of both grade and risk classes. A fusion method was used to combine all features extracted by the CNNs and make the final classification result. The classification accuracy of the grade and risk were 0.65 and 0.72, respectively. The classification results showed the proposed network fusion method can improve the performances on both task - DR grading and macular edema risk.
KW - Diabetic Retinopathy Severity
KW - Risk of Diabetic Macular Edema
KW - deep learning
KW - fundus image
KW - multi-CNNs
UR - http://www.scopus.com/inward/record.url?scp=85077957832&partnerID=8YFLogxK
U2 - 10.1109/ICPICS47731.2019.8942426
DO - 10.1109/ICPICS47731.2019.8942426
M3 - Conference contribution
AN - SCOPUS:85077957832
T3 - 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
SP - 463
EP - 466
BT - 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
Y2 - 12 July 2019 through 14 July 2019
ER -
