U-Net Based Retinal Vessel Detection on DRIVE and CHASE_DB1 Databases: The Impact of Data Augmentation

In this paper, we highlight image segmentation in the field of medical diagnosis, with a special emphasis on the accurate identification of retinal vessels in fundus images. Achieving accurate segmentation is essential for effective screening and monitoring of eye diseases. To enhance segmentation performance, the study presents a deep learning approach that utilizes the U-Net architecture. This method involves data augmentation preprocessing, where green channel extraction is performed along with Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement on two important databases: DRIVE [1] and CHASE_DB1 [2], to train the U-Net model. The research used Google Colab as the development environment and Python as the programming language. Experimental results confirm the effectiveness of this approach, providing promising avenues for further improvement and innovation in retinal vessel segmentation using the U-Net architecture.