Stacked Ensemble Network to Assess the Structural Variations in Retina: A Bio-marker for Early Disease Diagnosis

The retina is a unique tissue that extends the human brain in transmitting the incoming light into neural spikes. Researchers collaborating with domain experts proposed numerous deep networks to extract vessels from the retina; however, these techniques have the least response for micro-vessels. The proposed method has developed a stacked ensemble network approach with deep neural architectures for precise vessel extraction. Our method has used bi-directional LSTM for filling gaps in dis-joint vessels and applied W-Net for boundary refinement and emphasizing local regions to achieve better results for micro-vessels extraction. The platform has combined the strength of various networks to improve the automated screening process and has shown promising results on benchmark datasets.