Classification of glaucoma using hybrid features with machine learning approaches

Abstract

Glaucoma also known as silent theft of sight, is the prevailing source of sightedness across the globe. It is mainly caused owing to damage in the optical nerve of an eye leading to permanent blindness. Traditional approaches used by the ophthalmologists for diagnosis include assessment of intraocular pressure by tonometry, pachymetry etc. But all these evaluations are time-consuming, require human interaction and may be prone to subjective errors. Thus, to overcome these challenges, researchers are working in the field of medical imaging by analysis of the retinal images for glaucoma diagnosis. Also, Computer aided diagnosis (CAD) systems can be developed to overcome these challenges using machine learning approaches for classifying retinal images as ‘abnormal’ and ‘normal’. This paper presents a new set of reduced hybrid features derived from structural and nonstructural features to classify the retinal fundus image, which could serve as a second opinion for ophthalmologists. The structural features extracted include Disc damage likelihood scale (DDLS) and Cup to disc ratio (CDR). Whereas, non-structural features include Grey level run length matrix (GLRM), Grey level co-occurrence matrix (GLCM), First order statistical (FoS), Higher order spectra (HOS), Higher order cumulant (HOC) and Wavelets. Finally, the paper presents a comparative analysis of K nearest neighbor (k-NN), Neural network (NN), Random forest (RF), Support vector machine (SVM) and Naïve bayes (NB) using metrics such as accuracy, specificity, precision and sensitivity.