Class imbalance is a major problem when it comes to classification. The following diagram depicts the class densities of the five severity classes:
As we can see from the preceding chart, nearly 73% of the training data belongs to Class 0, which stands for no diabetic retinopathy condition. So if we happen to label all data points as Class 0, then we would have 73% percent accuracy. This is not desirable in patient heath conditions. We would rather have a test say a patient has a certain heath condition when it doesn't (false positive) than have a test that misses detecting a certain heath condition when it does (false negative). A 73% accuracy may mean nothing if the model learns to classify all points as belonging to Class 0.
Detecting the higher severity classes are more important...