Diabetic Retinopathy is a major cause of vision loss caused by retina lesions, including hard and soft exudates, microaneurysms, and hemorrhages. The development of a computational tool capable of detecting these lesions can assist in the early diagnosis of the most severe forms of the lesions and assist in the screening process and definition of the best treatment form. This paper proposes a computational model based on pre-trained convolutional neural networks capable of detecting fundus lesions to promote medical diagnosis support. The model was trained, adjusted, and evaluated using the DDR Diabetic Retinopathy dataset and implemented based on a YOLOv4 architecture and Darknet framework, reaching an mAP of 11.13% and a mIoU of 13.98%. The experimental results show that the proposed model presented results superior to those obtained in related works found in the literature.