RIB-NET AS MODIFICATION OF CNN ARCHITECTURE FOR SEMANTIC SEGMENTATION OF OPTIC DISC AND OPTIC CUP

Optic disc and optic cup segmentation are needed to detect glaucoma and diabetic retinopathy disorders in the retina. The separation of optic discs, optic cups, and other features in retinal images is semantic segmentation. Semantic segmentation requires a robust and valid architecture to be able to separate features in images accurately. This study proposes the RIB-Net architecture. The RIB-Net architecture is a modified U-Net architecture with residual blocks in the bridge section and inception block in each layer in the encoder and decoder sections. Residual blocks serve to overcome the problem of vanishing gradients and enable more effective information flow in bridge paths in U-Net. Inception blocks help recognize features of varying sizes in the encoder and decoder sections. This modification provides an architecture that has good capabilities in the semantic segmentation of more complex image features. The performance results of the RIB-Net architecture are accuracy of 99.21%, IoU of 55.32%, F1-score of 70.95%, sensitivity of 63.84%, and specificity of 99.71%. Other performance results from the RIB-Net architecture are α kappa coefficient of 0.758, MCC of 0.764, and G-means of 0.82. The RIB-Net architecture has a good balance between sensitivity and specificity, so it can be concluded that RIB-Net works well for optic disc and optic cup segmentation in retinal images. The results of the F1-score, IOU, and sensitivity still need to be improved so RIB-Net can be developed as a model for developing an automatic retinal image segmentation system.