Cataract Detection with YOLOv11: Hyperparameter Optimization and Performance Enhancement

Cataract is a common disease that causes vision loss due to the loss of transparency in the eye’s lens. The slow and subjective nature of traditional diagnostic methods has made automated image processing and deep learning solutions increasingly important. In this study, the YOLOv11 deep learning model was used to achieve fast and accurate results in cataract diagnosis. The model was preferred due to its high performance in object detection and classification. Hyperparameter optimization was applied to improve the model’s accuracy and generalization capacity, and the effects of optimization were evaluated through comparative analysis. Thanks to this approach, a more reliable diagnostic system has been developed.