TY - GEN
T1 - Automated Diagnosis Model for Glaucoma Detection
T2 - 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
AU - Sharma, Santosh Kumar
AU - Muduli, Debendra
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Glaucoma is an ocular disorder that affects millions of people and one of the second leading diseases in worldwide. Early detection of glaucoma is vital for avoiding irreversible vision loss problem. Our proposed model, based on deep learning-based ensemble pre-trained models like, Inception V3, GoogLeNet and VGG16 are compared with LS-SVM classifier to achieved better classification result (glaucoma or healthy) using G1020 dataset. Due to limited medical images in our dataset, we have applied data augmentation method is used to enhance the training size of the images in our preprocessing part. In our experimental result, we have observed that deep CNN-based ensemble pre-trained models with modified LS-SVM (least squares support vector machine) performed better classification result 93.90% using G1020 dataset as compared to other traditional models.
AB - Glaucoma is an ocular disorder that affects millions of people and one of the second leading diseases in worldwide. Early detection of glaucoma is vital for avoiding irreversible vision loss problem. Our proposed model, based on deep learning-based ensemble pre-trained models like, Inception V3, GoogLeNet and VGG16 are compared with LS-SVM classifier to achieved better classification result (glaucoma or healthy) using G1020 dataset. Due to limited medical images in our dataset, we have applied data augmentation method is used to enhance the training size of the images in our preprocessing part. In our experimental result, we have observed that deep CNN-based ensemble pre-trained models with modified LS-SVM (least squares support vector machine) performed better classification result 93.90% using G1020 dataset as compared to other traditional models.
KW - Convolutional neural network
KW - Deep learning
KW - Intraocular pressure
KW - Optical coherence tomography
UR - http://www.scopus.com/inward/record.url?scp=85179853730&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT56998.2023.10308246
DO - 10.1109/ICCCNT56998.2023.10308246
M3 - Conference contribution
AN - SCOPUS:85179853730
T3 - 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
BT - 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2023 through 8 July 2023
ER -