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To record anatomical details of the eye and anomalies, fundus imaging has proved very
efficient. The most effective way to see and diagnose a wide range of eye diseases is through
fundus imaging. Conditions that affect the blood vessels and areas surrounding it include diabetesrelated retinopathy, glaucoma, AMD, myopia, cataract and hypertension. It's possible for the
patient to have more than one ophthalmological problems that can be seen in one or both of
his eyes. The dataset provided by ODIR is used in this study. The data has eight different categories
for the diseases to be detected. By using transfer learning, two simultaneous models are described
for solving the multi label problem for both the eyes (left and right). For the convolutional network,
two synchronous efficient net models are implemented which are used with ADAM optimizers for
better detection and results outcome. On the ODIR data set, B7 Efficient net along with focal loss
outperformed the other approaches with an accuracy rate of 0.96%.