Comparative Analysis of the Fuzzy C-Means and Neuro-Fuzzy Systems for Detecting Retinal Disease

Image-processing methods are applied in many environments to solve various practical problems. Recently, considerable research has been conducted on analysing retinal images, which show the blood vessels in the human eye, to detect diseases in patients. Various diseases related to the eyes can be located by detecting changes in the retinal blood cells. This paper proposes two analysis methods, namely the fuzzy c-means and neuro-fuzzy methods, which analyse optical coherence tomography images of retinal blood cells. The system was implemented in a MATLAB experimental setup, while the efficiency was evaluated with respect to time and accuracy. The performance analysis of the fuzzy c-means and neuro-fuzzy systems indicates that the neuro-fuzzy system has a higher accuracy (93.16%) and a lower processing time (2 s) than the k-means clustering, expectation maximisation, fuzzy clustering and neural network methods.