GLAUCOMATOUS IMAGE CLASSIFICATION BY ADAPTIVE WAVELETS
DOI:
https://doi.org/10.61841/n2ayb632Keywords:
glaucomatous image classification by adaptive waveletsAbstract
Glaucoma disorder causes damage to a human's optic nerve due to the increased pressure in the eye. In this paper, an efficient method for glaucomatous image classification is presented using the Dual Tree M-band Wavelet Transform (DTMWT), Probabilistic Principal Component Analysis (PPCA), and Random Forest (RF) classifier. At first, DTMWT is applied to represent the fundus image in multi-resolution that contains lower and higher frequency components. The lower frequency components are reduced by PPCA, and then classification is made by an RF classifier. The device efficiency is reliably, sensitivity, and precisely calculated. Results show that a maximum classification accuracy of 91%, sensitivity of 88%, and specificity of 94% are obtained by PPCA-based DTMWT features with an RF classifier.
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