A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis

• Published in: Computers in biology and medicine Vol. 63; pp. 196 - 207
• Main Authors: Raja, Chandrasekaran, Gangatharan, Narayanan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2015; Elsevier Limited
• Subjects: Atoms & subatomic particles; Automation; Classification; Data mining; Diabetic retinopathy; Diagnosis, Computer-Assisted - methods; Feature extraction; Female; Fundus Oculi; Glaucoma; Glaucoma - diagnosis; Humans; Hybrid PSO–GSO; Hyper analytic wavelet transform; Hypotheses; Image Processing, Computer-Assisted - methods; Internal Medicine; Male; Mathematical functions; Multiculturalism & pluralism; Mutation; Optimization algorithms; Other; Population; Support Vector Machine; Support Vector Machines; Velocity; Wavelet transforms; Glaucoma; Feature extraction; Hyper analytic wavelet transform; Hybrid PSO–GSO; Support Vector Machines
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2015.05.018

Glaucoma is among the most common causes of permanent blindness in human. Because the initial symptoms are not evident, mass screening would assist early diagnosis in the vast population. Such mass screening requires an automated diagnosis technique. Our proposed automation consists of pre-processing, optimal wavelet transformation, feature extraction, and classification modules. The hyper analytic wavelet transformation (HWT) based statistical features are extracted from fundus images. Because HWT preserves phase information, it is appropriate for feature extraction. The features are then classified by a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. The filter coefficients of the wavelet transformation process and the SVM-RB width parameter are simultaneously tailored to best-fit the diagnosis by the hybrid Particle Swarm algorithm. To overcome premature convergence, a Group Search Optimizer (GSO) random searching (ranging) and area scanning behavior (around the optima) are embedded within the Particle Swarm Optimization (PSO) framework. We also embed a novel potential-area scanning as a preventive mechanism against premature convergence, rather than diagnosis and cure. This embedding does not compromise the generality and utility of PSO. In two 10-fold cross-validated test runs, the diagnostic accuracy of the proposed hybrid PSO exceeded that of conventional PSO. Furthermore, the hybrid PSO maintained the ability to explore even at later iterations, ensuring maturity in fitness.