Optic disc segmentation: Improved U-Net model and hyperparameter optimization with harmony search algorithm

Vision loss negatively impacts individuals quality of life, creating a societal issue. As stated by the World Health Organization, vision loss is significant concern among health problems. Glaucoma and Diabetic Retinopathy are among the primary causes of vision impairment and loss. Early diagnosis of these diseases is crucial for guiding appropriate treatment. Optical disc segmentation plays a key role in diagnosing these diseases and helps in effectively detecting them through the analysis of retinal images. This study proposes method based on the automatic processing of fundus images to contribute to the early diagnosis of eye diseases. Improved U-Net architecture developed for optical disc segmentation is used. To enhance the models performance, the improved U-Net architecture has been strengthened with residual and custom convolution blocks. Genetic Algorithm, Particle Swarm Optimization Algorithm, Ant Colony Optimization and Harmony Search Algorithm were used for hyperparameter optimization. Comparisons showed that the Harmony Search Algorithm achieved better results, improving overall model performance and providing more accurate segmentation outcomes. The models performance has been tested on the IDRiD and DRISHTI-GS datasets. The improved architecture outperformed existing methods in the literature. Significant improvements were observed in key segmentation metrics such as IoU and Dice coefficient. In experimental studies using the IDRiD and DRISHTI-GS datasets, the proposed model achieved IoU scores of 93.65% and 95.20% and Dice scores of 96.72% and 97.54% respectively. The model was able to accurately define optical disc boundaries even in low contrast and complex images. The obtained success demonstrates the models potential as valuable tool for the early diagnosis of vision loss causing diseases.