DIABETIC RETINOPATHY DETECTION AND RETINAL FEATURE OPTIMIZATION USING MACHINE LEARNING WITH INTELLIGENT CLASSIFICATION APPROACH
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Abstract
Diabetic retinopathy is a chronic eye disease that results from long-term diabetes and damages the blood
vessels in the retina which can cause vision loss if caught early enough. To detect this disease, retinal fundus
imaging is used and machine learning methods are used to analyse and classify the disease based on its severity.
In most cases, the overall process can be broken down into image acquisition, image preprocessing, feature
extraction, and classification, but traditional methods frequently suffer from image quality issues, noise, and the
inability to efficiently select the optimal features, all of which can compromise the accuracy and dependability of
classification. To address these issues, the proposed model uses the APTOS 2019 Blindness Detection dataset that
consists of labelled retinal images divided into 5 different stages of diabetic retinopathy. The proposed framework
has a structured workflow that enhances the image quality by removing the noise, resizing and enhancing the
contrast of the image, and then select the most relevant features of the retina using the Optimized Machine
Learning Feature Selection Algorithm (OMLFSA) for identification and selection. Intelligent Diabetic
Retinopathy Classification Algorithm (IDRCA) is the final classification process which is based on a machine
learning classification model that classifies the severity of DR. This holistic method will increase sensitivity of
detection and better diabetic retinopathy classification.
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