DRECQ: Design of an Efficient Model for Enhanced Diabetic Retinopathy Diagnosis Using Ensemble Classifiers and Deep Q Learning Process
Keywords:
Diabetic Retinopathy Detection, Ensemble Classifiers, Deep Q Learning, Retinal Image Analysis, Machine Learning, ScenariosAbstract
In addressing the critical need for advanced diagnostic tools in the realm of ophthalmology, particularly for the detection of diabetic retinopathy, this paper introduces a novel, ensemble-based approach, fusing the strengths of three distinct classifiers: Naive Bayes, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Traditional methods in retinal image analysis often fall short due to their static nature and inability to adapt to the unique complexities presented by individual images. This limitation manifests in less precise and accurate diagnostic outcomes, underscoring the urgent need for more dynamic and responsive techniques. The proposed model marks a significant departure from conventional approaches. By employing an ensemble method, it leverages the unique strengths of each classifier: the probabilistic analysis of Naive Bayes, the non-linear pattern recognition capability of SVM, and the intricate feature extraction proficiency of MLP process. The integration of these methods addresses the inherent limitations of using a singular approach, ensuring a more comprehensive analysis of retinal images & samples. Central to this innovation is the application of Deep Q Learning (DQL) for dynamic classifier selection. This reinforcement learning technique optimizes the ensemble by adaptively selecting the most suitable classifier for each specific retinal image, based on learned Q Values for different scenarios. This method not only enhances the accuracy and precision of diagnosis but also ensures continual adaptation and learning, keeping pace with evolving data patterns and advancements in imaging technology. The efficacy of this model is demonstrated through rigorous testing on the IDRiD & EyePACS Dataset. Results indicate a notable improvement over existing methods, with a 4.5% increase in precision, 5.5% in accuracy, 3.9% in recall, 4.9% in AUC (Area Under the Curve), 3.4% in specificity, and an 8.5% reduction in delay. These enhancements have profound implications for the field of ophthalmology. They signify a leap forward in the accuracy and timeliness of diabetic retinopathy diagnosis, ultimately leading to improved patient outcomes and a reduction in the burden on healthcare systems. This work, therefore, not only presents a technical advancement but also a significant stride in patient care, paving the way for more effective management and treatment of retinal diseases.
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