Using soft computing techniques to diagnose Glaucoma disease
Glaucoma is a major cause of blindness. Most patients start to observe that late after the disease causes a high level of damage in the optic nerve head and the high percentage of vision loss. Early diagnosis and treatment are essential and must be taken. Image processing mass-screening and machine...
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| Main Authors: | , , , |
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| Format: | Book |
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Elsevier,
2021-01-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | Glaucoma is a major cause of blindness. Most patients start to observe that late after the disease causes a high level of damage in the optic nerve head and the high percentage of vision loss. Early diagnosis and treatment are essential and must be taken. Image processing mass-screening and machine learning classification can support early and automatic diagnosis of Glaucoma symptoms so as to take protective measures and to extend symptom-free life of patients. This paper proposes improved techniques to extract disease-related and image-based features. Support Vector Machines and Genetically-Optimized Artificial Neural Networks, pronounced machine learning algorithms, are fine-tuned to combine the two set of features in one automated image classification system. The proposed methodology was applied to a dataset of 106 retina images obtained from three hospitals. The proposed system automatically detected Glaucoma using Support Vector Machines technique with 100% specificity and 87% accuracy. Artificial Neural Network classified the images with 98% accuracy. |
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| Item Description: | 1876-0341 10.1016/j.jiph.2019.09.005 |