Retinal (Glaucoma) Image Synthesis
Using Traditional and Advanced Data Augmentation Techniques
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The human eye is an organ that reacts to light and pressure. Many diseases, disorders, and age-related changes may affect the eyes and surrounding structures. One of the eye diseases is glaucoma. Glaucoma is a condition in which fluid pressure rises within the eye. Without treatment, it can damage the optic nerve and lead to vision loss. The early detection of glaucoma minimizes the risk of vision loss. The proposed model synthesizes highly realistic controllable fundus images to obtain precision in detecting glaucoma through a deep learning model.A generative adversarial network (GAN) is an u...
The human eye is an organ that reacts to light and pressure. Many diseases, disorders, and age-related changes may affect the eyes and surrounding structures. One of the eye diseases is glaucoma. Glaucoma is a condition in which fluid pressure rises within the eye. Without treatment, it can damage the optic nerve and lead to vision loss. The early detection of glaucoma minimizes the risk of vision loss. The proposed model synthesizes highly realistic controllable fundus images to obtain precision in detecting glaucoma through a deep learning model.A generative adversarial network (GAN) is an unsupervised machine learning technique that can be used to augment datasets and yield collected images to be indistinguishable from real-world data. The Deep Convolutional GAN (DCGAN), another variant of GAN, suggests the architectural constraints on the model required to develop high-quality generator models effectively. The enhanced dataset, obtained from data augmentation and the originalACRIMA dataset of fundus images, are separately given to the CNN classification model for detecting glaucoma disease.
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