Joint Runet++: A Joint Region-Based Unet++-Based Optic Disc and Cup Segmentation with Ensemble Generalization Loss for Glaucoma Disease Prediction
Keywords:
Convolutional neural network (CNN), Joint region-based Unet (JointRUnet ), Respective regions of interest (ROI), Disc detection network (DDN), Cup detection network (CDN), Optic cup (OC), Optic disc (OD)Abstract
Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup-to-disc ratio (CDR) plays an important role in diagnosing and screening glaucoma. Early detection of glaucoma is key to preventing vision loss, but there is a lack of recognizable early symptoms. In this paper, the proposed joint region-based U net++ (Joint R U net++) is used to segment the optic disc (OD) and the optic cup (OC) at the same time. The proposed Joint RUnet++ contains an attention-driven serial Unet++-based feature extraction module, a disc detection network (DDN), and a cup detection network (CDN). This DDN and CDN network contains an ROI detection network that crops the input feature maps based on the coordinates of bounding boxes. The Deep Net Ensemble model consists of three CNNs: VGG19, DarkNet19, and EfficientNet-B1 trained on multiple projections of retinal fundus images. Integrate Hybrid Beetle Antenna Search and Genetic Algorithm (HBAS-GA) to detect optimized fusion weights from deep-net models using deep-ensemble generalization losses. The experiment was built on RIM-ONE DL, refuge and real datasets. A comparison is made between the experimental results and previous prediction models in terms of accuracy, specificity, sensitivity, IOU slice, IOU cup and ROC. The Rim One dl has an accuracy score of 0.981, the refuge dataset has an accuracy score of 0.986, and the real-time dataset has an accuracy score of 0.986.
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