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- MakaleYalnız künye2026Optic disc segmentation: Improved U-Net model and hyperparameter optimization with harmony search algorithm
İncir, Ramazan; Bozkurt, Ferhat
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.
- MakaleYalnız künye2026A Comprehensive Deep-Learning Framework Integrating Lesion Segmentation and Stage Classification for Enhanced Diabetic Retinopathy Diagnosis
İncir, Ramazan; Bozkurt, Ferhat
Diabetic retinopathy (DR), one of the most prevalent microvascular complications of diabetes, stands as a leading cause of vision loss globally. Due to its asymptomatic nature in early stages, delayed diagnosis and staging may result in irreversible visual impairment. Therefore, accurate and simultaneous lesion segmentation and stage classification of DR are of critical clinical importance. In this study, a two-stage, end-to-end, holistic framework is proposed for automated DR diagnosis. In the first stage, an Improved U-Net architecture enhanced with residual blocks and additional convolutional layers is employed to segment small and low-contrast lesions such as microaneurysms, hemorrhages, and hard/soft exudates with high precision. Model hyperparameters are optimized using the harmony search algorithm to enhance training efficiency. In the second stage, lesion-based weight maps obtained from the segmentation step are applied to fundus images from the APTOS dataset, generating enriched inputs for classification. A vision transformer (ViT)-based model, augmented with a Convolutional Block Attention Module (CBAM), is utilized to improve feature extraction. In addition, features derived from ViT are further refined using a graph convolutional network (GCN) and traditional machine-learning classifiers. The proposed approach achieves high performance in multi-class DR stage classification. Compared to existing studies, the framework demonstrates notable improvements in both segmentation and classification accuracy, offering a robust and generalizable solution for DR diagnosis.