TY - GEN
T1 - Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image
AU - Rajinikanth, Venkatesan
AU - Kadry, Seifedine
AU - Damasevicius, Robertas
AU - Taniar, David
AU - Rauf, Hafiz Tayyab
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/25
Y1 - 2021/3/25
N2 - Eye is a fundamental sensory organ and any disease in eye will severely affect the sensory signal evaluation and conclusion making capability of the brain. The Choroidal-Neovascularization (CNV) is one of the harsh eye diseases in which a new blood-vessel grow from the choroid. Usually, the major cause of CNV is due to wet Age-Related-Macular-Degeneration (ARMD) and the formed new vessel will cause a leak in fluid which makes the retinal wet. The untreated CNV will lead to vision loss. In this research, detection of CNV using Optical-Coherence-Tomography (OCT) is presented using 484 images (242 Healthy and 242 CNV). In this work, a Machine-Learning-Scheme (MLS) is developed to examine the resized OCT of 256x256 pixels and the stages of this MLS includes; pre-processing, feature extraction, Mayfly-Optimization-Algorithm (MFA) based feature reduction, and two-class classification. The experimental outcome of this technique confirmed that the Fine-Gaussian-SVM (SVM-FG) classifier helped to accomplish an improved classification accuracy (>92%) compared to the alternative classifiers of this study.
AB - Eye is a fundamental sensory organ and any disease in eye will severely affect the sensory signal evaluation and conclusion making capability of the brain. The Choroidal-Neovascularization (CNV) is one of the harsh eye diseases in which a new blood-vessel grow from the choroid. Usually, the major cause of CNV is due to wet Age-Related-Macular-Degeneration (ARMD) and the formed new vessel will cause a leak in fluid which makes the retinal wet. The untreated CNV will lead to vision loss. In this research, detection of CNV using Optical-Coherence-Tomography (OCT) is presented using 484 images (242 Healthy and 242 CNV). In this work, a Machine-Learning-Scheme (MLS) is developed to examine the resized OCT of 256x256 pixels and the stages of this MLS includes; pre-processing, feature extraction, Mayfly-Optimization-Algorithm (MFA) based feature reduction, and two-class classification. The experimental outcome of this technique confirmed that the Fine-Gaussian-SVM (SVM-FG) classifier helped to accomplish an improved classification accuracy (>92%) compared to the alternative classifiers of this study.
KW - Choroidal-Neovascularization
KW - Classification
KW - Mayfly-Optimization-Algorithm
KW - OCT image
KW - Retinal OCT
UR - https://www.scopus.com/pages/publications/85107908681
U2 - 10.1109/ICBSII51839.2021.9445134
DO - 10.1109/ICBSII51839.2021.9445134
M3 - Conference contribution
AN - SCOPUS:85107908681
T3 - Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
BT - Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
Y2 - 25 March 2021 through 27 March 2021
ER -