Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and CorrentropyFeatures Extracted from Fundus Images


Glaucoma is a visual issue caused because of expanded liquid weight in the optic nerve. It harms the optic nerve and in this way causes loss of vision. The accessible checking strategies are Heidelberg retinal tomography, examining laser polarimetry, and optical cognizance tomography. These techniques are costly and require experienced clinicians to utilize them. Along these lines, there is a need to determine glaucoma precisely to have minimal effort. Consequently, in this, we have exhibited another technique for a mechanized determination of glaucoma utilizing computerized fundus pictures dependent on experimental wavelet change (EWT).

The EWT is utilized to break down the picture, and correntropy highlights are acquired from decayed EWT parts. These extricated highlights are positioned dependent on t esteem include determination calculation. At that point, these highlights are utilized for the grouping of ordinary and glaucoma pictures utilizing slightest squares bolster vector machine (LS-SVM) classifier. The LS-SVM is utilized for grouping with spiral premise work, Morlet wavelet, and Mexican-cap wavelet parts. The order precision of the proposed strategy is 98.33% and 96.67% utilizing triple and ten times cross approval, individually.

BASE PAPER: Earlier Detection of Glaucoma

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