Glaucoma Retinal Image Classification Based on Multichannel Gabor Filtering and Transfer Learning

The retina is affected by glaucoma and diabetic retinopathy (DR). Glaucoma must be detected early because it is irreversible and one of the leading causes of blindness. A delayed diagnosis will result in permanent vision loss. It is characterized primarily by ganglion cell dysfunction, which changes the thickness of the retinal nerve fiber layer and the shape of the optic nerve head. As a result, early detection of glaucoma is critical for preventing vision loss. This study employs a hybrid approach to glaucoma diagnosis by combining its powerful Multichannel Gabor filtering and Principal Component Analysis (PCA) capabilities with various transfer learning architectures such as MobileNet, MobileNetV2, and NASNet. These classifiers divide the source retinal images into two groups: glaucoma and non-glaucoma. The suggested approaches are used and evaluated on a dataset of Retinal Fundus Images. For the glaucoma diagnostic method, this strategy yields 99% Precision, 97% Recall, and 98% Accuracy.