Abstract
A neuro-degenerative eye disease called Glaucoma is expanded due to the development of ocular pressure inside the retina. Worldwide, glaucoma is the second widest reason for increased vision loss. Moreover, glaucoma affects millions of people, and early intervention is crucial to prevent irreversible vision loss. The absence of an early diagnosis causes complete blindness to the individuals. Furthermore, screening entire populations for glaucoma is impractical due to its low prevalence in communities, the lack of cost-effectiveness, and various logistical challenges. Therefore, the above-mentioned issues are overcome by this investigation proposing an effectual detection scheme called Shepard Convolutional Kronecker Network (ShCKN) for glaucoma detection. Initially, the input glaucoma image undergoes preprocessing with a Gaussian filter to eliminate unwanted noise. The preprocessed image is then fed into the segmentation module, where optic disc segmentation is achieved using Bayesian fuzzy clustering (BFC), followed by blood vessel detection through a sparking process. Significant features, such as local gradient binary pattern (LGBP), convolutional neural network (CNN) features, and statistical features, are then extracted. Finally, glaucoma detection is conducted using the ShCKN, which integrates Shepard convolutional neural networks (ShCNN) and deep Kronecker network (DKN), with layer modifications based on fuzzy concepts. Furthermore, the proposed ShCKN model achieved the highest accuracy of 91.8%, specificity of 91.4%, sensitivity of 91.2%, and Matthews correlation coefficient (MCC) of 88.3%.
Published Version
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