A Retinal Image Processing and Segmentation for Diabetic Retinopathy Detection using Deep Learning-Based Techniques
Keywords:
CLAHE, DR, DNCNN, Segmentation, STAREAbstract
Retinal image analysis is an essential tool for diagnosing various eye diseases, including diabetic retinopathy. However, the presence of noise and low contrast in retinal images can lead to inaccurate analysis and diagnosis. In this paper, we propose a preprocessing and segmentation pipeline for retinal images that incorporates adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE), Deep Convolutional Neural Network (DNCNN), and Otsu Thresholding. The proposed method effectively removes noise and enhances the contrast in retinal images, leading to accurate segmentation of the optic disc and blood vessels. The experimental results demonstrate that the proposed pipeline outperforms existing state-of-the-art methods for retinal image preprocessing and segmentation, achieving high accuracy and robustness. The proposed method can be a valuable tool for automatic diagnosis and monitoring of diabetic retinopathy and other retinal diseases.
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