Spatial Attention Enhanced Network for Segmentation of Exudate

Diabetic retinopathy is a disease allied with prolonged diabetes mellitus, in which the eyes get affected. An early indication of the disease is the formation of exudate which arises as a result of the vascular leakage of lipid. The condition may lead to blindness if it develops near the macula. Diagnosis of exudate at its initial phase is likely to lower the chance of visual impairment in patients. A major problem in detecting exudate is its small size in comparison to the total image area. This study introduces a method for segmenting exudate in fundus images considering an encoder-decoder architecture which incorporates the concept of Spatial Attention mechanism. To enhance the performance of the model and to compute the spatial relationship among the features, the spatial attention mechanism is applied. The efficiency of the proposed network is assessed using four datasets namely DIARETDB0, DIARETDB1, MESSIDOR, and IDRiD. It has been noticed that the designed framework attends an overall accuracy of 94.98% and also performs better over the existing methods considering positive predictive value, specificity, and sensitivity as segmentation metrics.