This paper presents a framework for the automated detection of Exudates, an early sign of Diabetic Retinopathy. The paper introduces a classification-extraction-superimposition (CES) mechanism for enabling the generation of representative exudate samples based on limited open-source samples. The paper demonstrates how the manipulation of Yolov5M output vector can be utilized for exudate extraction and super-imposition, segueing into the development of a custom CNN architecture focused on exudate classification in retinal based fundus images. The performance of the proposed architecture is compared with various state-of-the-art image classification architectures on a wide range of metrics, including the simulation of post deployment inference statistics. A self-label mechanism is presented, endorsing the high performance of the developed architecture, achieving 100% on the test dataset.