Early fundus screening is a cost-effective and efficient approach to reduce ophthalmic disease-related blindness in ophthalmology. Manual evaluation is time-consuming. Ophthalmic disease detection studies have shown interesting results thanks to the advancement in deep learning techniques, but the majority of them are limited to a single disease. In this paper we propose the study of various deep learning models for eyes disease detection where several optimizations were performed. The results show that the best model achieves high scores with an AUC of 98.31% for six diseases and an AUC of 96.04% for eight diseases.