Combining Machine Learning and Deep Learning in the Retinopathy Diagnostic Algorithm for Enhanced Detection of DR and DME
DOI:
https://doi.org/10.52783/jns.v14.2914Keywords:
Retinopathy Diagnostic Algorithm, Machine Learning, Deep Learning, Automated Diagnosis, Convolutional Neural Networks, Medical ImagingAbstract
Diabetic Retinopathy and Diabetic Macular Edema are severe retinal complications affecting millions worldwide, especially within the diabetic population. These conditions, if left untreated, can lead to significant vision impairment and even blindness, emphasizing the importance of early and accurate diagnosis. Traditional diagnostic methods, typically reliant on manual inspection of retinal images, are time-consuming, resource-intensive, and susceptible to subjective variability, highlighting a critical need for automated and precise diagnostic solutions.
This research article introduces the Retinopathy Diagnostic Algorithm (RDA), an integrated machine learning and deep learning framework designed to enhance the accuracy and efficiency of DR and DME diagnosis. RDA effectively combines robust feature extraction with sophisticated pattern recognition capabilities, enabling the precise classification and identification of retinal anomalies indicative of DR and DME. The proposed algorithm utilizes a hybrid approach where deep convolutional neural networks (CNNs) perform initial feature extraction, followed by classification using a machine learning model optimized for medical image analysis.
Experimental results demonstrate the superior diagnostic accuracy of RDA compared to traditional standalone ML or DL models. Key performance metrics indicate that RDA not only improves diagnostic accuracy but also increases sensitivity and specificity, critical measures in clinical diagnostics. The RDA framework shows promise in reducing diagnostic errors and supporting early detection, ultimately contributing to improved patient outcomes. This research underscores the potential of integrated ML-DL approaches in advancing automated diagnostics and provides a scalable, clinically applicable solution for DR and DME detection.
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