Precise grading of diabetic retinopathy and glaucoma: a novel DCNN and modified semantic segmentation method

Diabetic retinopathy and glaucoma are significant causes of blindness worldwide, affecting one-third of diabetic patients and indicating an increased risk of vascular complications. This study proposes a modified DeepLabV3+ semantic segmentation model for segmentation and a deep convolutional neural network (DCNN) model to classify and grade diabetic retinopathy and glaucoma, namely, the modified DeepLabV3+ segmentation and classification model (MDSCM). This study utilises two proposed datasets, namely, KEH_3815 and KEH_4129, which contain 8000 images collected from Khulna Eye Hospital and Laser Centre, Khulna, Bangladesh, and eight other benchmark datasets to evaluate the proposed methods. The proposed modified DeepLabV3+ semantic segmentation model achieves 3.69%, 2.92% and 5.38% higher accuracy, F1-score and Jaccard index than the original DeepLabV3+ semantic segmentation model in diabetic retinopathy segmentation. Similarly, for glaucoma, the modified DeepLabV3+ semantic segmentation model achieves 3.07%, 3.19% and 6.11% higher accuracy, F1-score and Jaccard index, respectively, than the original DeepLabV3+ semantic segmentation model. For the diabetic retinopathy dataset, the proposed DCNN classification and grading model achieves 98.63%, 98.61%, 99.58% and 99.09% accuracy, precision, recall and F1-score, respectively. Similarly, for glaucoma, the proposed DCNN classification and grading model achieves 97.81%, 96.67%, 98.32% and 97.49% accuracy, precision, recall and F1-score, respectively. By comparing the performances, the proposed model outperforms existing research.