Published Paper Details:

The escalating prevalence of diabetes globally has positioned it as a significant contributor to mortality rates. Elevated blood glucose levels and insulin resistance are implicated in a spectrum of health complications, encompassing cardiovascular diseases, renal dysfunction, neuropathies, and diabetic retinopathy--a debilitating condition marked by vision impairment. Early detection serves as a pivotal determinant in mitigating the progression of diabetic retinopathy and averting irreversible visual impairment. Consequently, the imperative for a robust screening methodology becomes apparent. To this end, a comprehensive investigation into deep learning strategies has been undertaken. Leveraging publicly available datasets, our research ends have focused on the acquisition and reprocessing of data pertaining to diabetic individuals. Furthermore, the development of sophisticated machine learning algorithms, including deep neural networks such as Convolutional Neural Networks (CNNs), has been instrumental in refining our screening approach. The culmination of these efforts has yielded promising outcomes, as evidenced by the system's proficiency in accurately discerning normal retinal images from those indicative of aberrant pathology with a remarkable degree of certainty. In conclusion, the integration of advanced technologies, such as deep learning and machine learning, holds considerable promise in revolutionizing diabetic retinopathy screening protocols. By harnessing the power of these methodologies, we can enhance early detection, facilitate timely intervention, and ultimately ameliorate the burden of diabetic retinopathy on global visual health outcomes