Diabetic detection from images of the eye

This cross-sectional study aims to detect Diabetic Retinopathy (DR) in patients who have had retinal scans and ophthalmological exams. The research makes use of tailored retinal images together with the OPF (Optimum-Path Forest) and RBM (Restricted Boltzmann Machine) models to categorize images according to the presence or absence of DR. In this work, features were extracted from the retinal images using both the RBM and OPF models. In particular, after a thorough system training phase, RBM was able to extract between 500 and 1000 features from the images. The study included fifteen distinct trial series, each with thirty cycles of repetition. The research comprised 122 eyes, or 73 diabetic patients, with a gender distribution that was reasonably balanced and an average age of 59.7 years. Remarkably, the RBM-1000 model stood out as the top performer, with the highest overall accuracy of 89.47% in diagnosis. In terms of specificity, the RBM-1000 and OPF-1000 models surpassed the competition, correctly categorizing all images free of DR symptoms. These findings highlight the potential of machine learning, particularly the RBM model, for self-identifying illnesses. The potential of machine learning models—in particular, RBM and OPF—to automate the diagnosis of diabetic retinopathy is demonstrated by this work. The results show how well the RBM model diagnoses, how sensitive it is, and how well it can be applied for efficient DR screening and diagnosis. This information may be used to improve the effectiveness of systems that identify retinal illnesses.