Diabetic Retinopathy Prediction Using Soft Computing-Based Fuzzy-SVM Integrated Diabetic Retinopathy Prediction Framework (SC-FSIDR-PF)
Keywords:
Soft computing, fuzzy-logic, Support Vector Machine, Improved Non-local Means filter, Gray-Level Co-occurrence Matrix, Principal Component Analysis.Abstract
Diabetic retinopathy (DR) is a severe eye condition caused by diabetes, leading to vision impairment and blindness if not detected early. This study proposes a novel hybrid model integrating soft computing techniques, specifically Fuzzy Logic and Support Vector Machine (SVM), for predicting DR from retinal images. Initially, retinal images from the Indian Diabetic Retinopathy Image (IDRiD) dataset are collected and pre-processed using the Improved Non-local Means (INLM) filter for noise reduction. Feature extraction is performed using the Gray-Level Co-occurrence Matrix (GLCM), followed by dimensionality reduction with Principal Component Analysis (PCA). The fuzzy logic system processes these features, handling the inherent uncertainty and imprecision in medical data and outputs fuzzy values representing the health state. These outputs are then fed into an SVM classifier, which employs a kernel function to handle non-linearity and separates the data into healthy and DR-affected categories. This hybrid approach leverages the interpretability of fuzzy logic and the classification strength of SVM, resulting in a robust and accurate predictive model for Early detection of diabetic retinopathy is possible with soft computing techniques.
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