TY - GEN
T1 - Glaucoma Detection Based-on Convolution Neural Network and Fundus Image Enhancement
AU - Haq, Dina Zatusiva
AU - Awwabi, Labba
AU - Hidayati, Shintami Chusnul
AU - Herumurti, Darlis
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The number of glaucoma sufferers is expected to increase to around 111.8 million in 2040 due to aging and population growth. It is necessary to develop technology to detect glaucoma automatically with a high degree of accuracy and efficiency. This research made the glaucoma classification system by comparing CNN architecture (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101) and implementing the CLAHE method as a pre-processing step. The evaluation system based on accuracy shows that the implementation of CLAHE can improve accuracy by about one to two percent. The average accuracy on Resnet-101 reached 88.24%, followed by average accuracy of ResNet-50, ResNet-18, AlexNet, and GoogleNet architectures with 87.82 %, 86.01%, 85.68%, and 85.63% accuracy, respectively. From all experiments, the best accuracy result is 89.59% on the ResNet-50 architecture experiment with a batch size of 16.
AB - The number of glaucoma sufferers is expected to increase to around 111.8 million in 2040 due to aging and population growth. It is necessary to develop technology to detect glaucoma automatically with a high degree of accuracy and efficiency. This research made the glaucoma classification system by comparing CNN architecture (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101) and implementing the CLAHE method as a pre-processing step. The evaluation system based on accuracy shows that the implementation of CLAHE can improve accuracy by about one to two percent. The average accuracy on Resnet-101 reached 88.24%, followed by average accuracy of ResNet-50, ResNet-18, AlexNet, and GoogleNet architectures with 87.82 %, 86.01%, 85.68%, and 85.63% accuracy, respectively. From all experiments, the best accuracy result is 89.59% on the ResNet-50 architecture experiment with a batch size of 16.
KW - CLAHE
KW - CNN
KW - Deep Learning
KW - Glaucoma
KW - Image Enhancement
UR - http://www.scopus.com/inward/record.url?scp=85141565368&partnerID=8YFLogxK
U2 - 10.1109/ICoICT55009.2022.9914849
DO - 10.1109/ICoICT55009.2022.9914849
M3 - Conference contribution
AN - SCOPUS:85141565368
T3 - 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
SP - 6
EP - 11
BT - 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information and Communication Technology, ICoICT 2022
Y2 - 2 August 2022 through 3 August 2022
ER -