Early Detection of Diabetic Retinopathy Utilizing Advanced Fuzzy Logic Techniques

Abstract

The escalating prevalence of diabetes globally, exacerbated by lifestyle changes post-pandemic—including increased screen time, sedentary behavior, and remote work— has consequently driven a surge in associated complications, notably, Diabetic Retinopathy (DR). This ocular complication presents a pressing concern due to its potential to precipitate irreversible vision loss. Consequently, the necessity for timely and accurate DR detection is paramount, especially in circumstances where conventional diagnostic approaches are either challenging or financially prohibitive. Capitalizing on the prowess of fuzzy logic in managing uncertainties, this study introduces an innovative application of Extended Fuzzy Logic for the early-stage detection of DR. Rather than focusing solely on overt symptoms, this approach discerns subtle similarities in retinal irregularities between diabetic patients and non-diabetic individuals. To quantify these similarities, the ‘f-validity’ value was computed based on DR risk factors and associated symptoms, which were subsequently transformed into membership function values. The aggregation of these values was facilitated by the Ordered Weighted Averaging (OWA) operator. The experimental outcomes of this approach align satisfactorily with expert anticipations, boasting an accuracy of 90%, a precision of 92.2%, and a sensitivity of 75%. These results, when juxtaposed against contemporary studies in the field, underscore the promise of this scheme in advancing early diagnostics of DR. The study thus proposes a potential solution that leverages the power of fuzzy logic to address the burgeoning challenge of DR.