Deep Vision: Iris Segmentation for Diagnostic Insights in Iridology

Ocular diseases, particularly diabetic retinopathy, are increasingly prevalent and necessitate precise detection methods. The proposed method, “Deep Vision: Iris Segmentation for Diagnostic Insights in Iridology,” leverages deep learning for enhanced prediction of diabetes-related ocular conditions. Data from the diabetes-iridology dataset is pre-processed with Contrast Limited Adaptive Histogram Equalization (CLAHE) [1]. The Attenuation-based U-Net model [2] ensures precise iris segmentation. For improved classification, DenseNet [3], EfficientNet [4], and Capsule Networks (CapsNet) [5] are evaluated. The Python-based implementation achieves an impressive accuracy rate of 95.87% [6], demonstrating a substantial improvement over conventional method. This approach not only advances diagnostic performance but also sets a new benchmark for precision in ocular disease prediction.