A supervised machine learning algorithm SKVMs used for both classification and screening of glaucoma disease

Glaucoma is the second leading cause of vision loss in the world. We propose a novel, automated, appearance-based glaucoma classification system that does depend on segmentation-based measurements. It applies a standard pattern recognition process with a 2-stage classification step: To automatically extract the optic disc (OD), two methods making use of an edge detection method and Contours active Chan and Vese model are proposed in this paper. For the optic cup (OC) or excavation, inspection by the histogram is used to automatically detect the (OC). Our system SKVMs technique achieves 93% success rate on a data set containing a mixture of 75 real images of healthy and glaucomatous eyes. A set of 75 retinal images obtained from healthy and glaucomatous eyes, is used to assess the performance of the determined CDR to the clinical CDR, and it is found that our proposed method provides 98% accuracy in the determined CDR results and an early screening glaucoma by SKVMs approach that presented the aim of this paper. Abbreviations: AANN: Artificial network of neurons; ANOVA: Analysis of variance; C-CHT: Circular Hough transform; CDR: Cupto-Disc ratio; COG: Concept centre of gravity; Df: Degree of freedom; Dice: The Dice index measures the similarity between two images d1 and d2 based on the number of regions common to d1 and d2; E-EOD: Examination of optic disc; EDA: estimation of distribution algorithms; F-FP: False positive; FN: False negative; FPT: The false positive rate; FBR: The basic radial function; FOV: The field of view; G-GPAO: Primary open-angle glaucoma; Gdx: The GDx is a tool that uses the laser to determine the thickness of the nerve fibre layer; H-HRT: retinal tomography of Heidelberg; HSD: “Honestly Significantly Different” test; IICE: Iridocorneal endothelium syndrome; ISNT: Lower-Upper-Nasal-Temporal; J-Jaccard: The index and the distance of Jaccard are two metrics used in statistics to compare the similarity and the diversity (in) between samples. They are named after the Swiss botanist Paul Jaccard. The Jaccard index or Jaccard coefficient [Jaccard, 1901] is the ratio between the cardinality (size) of the intersection of the sets considered and the cardinality of the union of the sets. It makes it possible to evaluate the similarity between the sets; K-KS: The Kolmogorov-Smirnov test; MMSE: Mean square error; N-NTG: Glaucoma at normal tension; NIR: near infrared light (NIR); NPBS: Collimation to a non-polarizing beam splitter; O-OC: Optic Cup; OD: Optic Disc; OCT: ocular coherence tomography; ONH: Optic Nerve Head; P-PIO: Intraocular pressure; PBS: The polarizing beam splitter; PSG: the polarization state generator; PSD: The polarization state detector; PXF: pseudoexfoliative glaucoma, also called exfoliative glaucoma; RROI: Region of interest; RGB: Red_Green_Blue channels; ROC: The characteristic operating curve of the receiver; RBS: Renal Birefringence Scanning; S-SKVMs: Support kernel vector machines; SNR: Signal-to-noise ratio; Std: Standard deviation; SLO: Scanning Laser Ophthalmology; SLP: Scanning Laser Polarimetry (SLP); TN: *Correspondence to: Rached Belgacem, Department of biophysics, Laboratory of Research in Biophysics and Medical Technologies LRBTM, Higher Institute of Medical Technologies of Tunis ISTMT, University of Tunis El Manar, 9 Avenue Dr. Zouheïr Safi _1006, Tunis, Tunisia, E-mail: rachedbel@gmail.com