HIGHLIGHTS
SUMMARY
Supervised methods are learned based on the features of the input images that are manually marked. In contrast, unsupervised methods discover hidden features and do not require manually segmented images. B. KEY RETIINAL IMAGE FEATURES AND PATHOLOGIES This section defines the primary retinal features and pathologies and discusses their significance in automated retinal image analysis. The authors divided supervised methods into SVM, artificial neural_networks (ANNs), and miscellaneous methods. The performance of the model was assessed using three open-access retinal image databases: DRIVE, CHASE_DB1, and STARE. The authors retrained the ColonSegNet model on retinal . . .
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