Leveraging Convolutional Neural Networks for Automated Detection and Grading of Diabetic Retinopathy from Fundus Images

Abstract

This study addresses the critical challenge of Diabetic Retinopathy (DR) detection and severity grading, aiming to advance the field of medical image analysis. The research problem focuses on the need for an accurate and efficient model to discern DR conditions, thereby facilitating early diagnosis and intervention. Employing a Convolutional Neural Network (CNN), our methodology is developed to strike a balance between precision and computational efficiency, a pivotal aspect in the context of healthcare applications. The research leverages the APTOS 2019 dataset, a comprehensive collection of fundus photographs, to evaluate the efficacy of our proposed model. The dataset allows for a thorough investigation into the model's performance in binary-class and multi-class classifications, providing a robust foundation for analysis. The most important result of our study manifests in the achieved accuracy rates of 98.67% and 87.81% for binary-class and multi-class classifications, respectively. These outcomes underscore the model's reliability and innovation, surpassing established machine learning algorithms and affirming its potential as a valuable tool for early DR detection and severity assessment. In conclusion, the study marks a significant advancement in leveraging deep learning for ophthalmic diagnoses, particularly in the nuanced landscape of Diabetic Retinopathy. The implications of our findings extend to the broader realm of AI-driven healthcare solutions, presenting opportunities for enhanced clinical practices and early intervention strategies. Future research endeavors could explore further refinements to the model, considering additional datasets and collaborating with healthcare professionals for real-world validation, ensuring the continued progress of AI applications in the medical domain.