Authors :
Dr. M.Suresh; G. Likhitha; G. Yogeeswar; B. Sasank Kalyan; Ch. Lakshmi Bhavana
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/55aupzcc
Scribd :
https://tinyurl.com/ajcrnref
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR678
Abstract :
This research project focuses on the development
and evaluation of an advanced algorithm for retinal vessel
segmentation, a critical component in the automated
analysis of retinal images for diagnosing ocular diseases.
Leveraging state-of-the-art image processing techniques
and deep learning models, we propose a novel segmentation
algorithm that significantly enhances the accuracy and
efficiency of identifying retinal blood vessels from fundus
photographs. Our methodology encompasses a
comprehensive data preparation phase, including image
normalization and augmentation, to improve the model's
robustness and generalizability. We implemented a
convolutional neural network (CNN)-based architecture
optimized for the intricate patterns and variations inherent
in retinal images. The performance of our algorithm was
rigorously evaluated against established benchmarks,
demonstrating superior precision, recall, and a higher Dice
coefficient compared to existing methods. These findings
indicate the potential of our approach to contribute
substantially to the early detection and monitoring of ocular
conditions such as diabetic retinopathy and glaucoma. The
research underscores the importance of advanced
computational techniques in enhancing the diagnostic
capabilities of retinal image analysis and sets the stage for
future innovations in medical imaging.
Keywords :
Retinal Vessel Segmentation, Deep Learning, Image Processing, Diabetic Retinopathy, Convolutional Neural Networks (CNNs).
This research project focuses on the development
and evaluation of an advanced algorithm for retinal vessel
segmentation, a critical component in the automated
analysis of retinal images for diagnosing ocular diseases.
Leveraging state-of-the-art image processing techniques
and deep learning models, we propose a novel segmentation
algorithm that significantly enhances the accuracy and
efficiency of identifying retinal blood vessels from fundus
photographs. Our methodology encompasses a
comprehensive data preparation phase, including image
normalization and augmentation, to improve the model's
robustness and generalizability. We implemented a
convolutional neural network (CNN)-based architecture
optimized for the intricate patterns and variations inherent
in retinal images. The performance of our algorithm was
rigorously evaluated against established benchmarks,
demonstrating superior precision, recall, and a higher Dice
coefficient compared to existing methods. These findings
indicate the potential of our approach to contribute
substantially to the early detection and monitoring of ocular
conditions such as diabetic retinopathy and glaucoma. The
research underscores the importance of advanced
computational techniques in enhancing the diagnostic
capabilities of retinal image analysis and sets the stage for
future innovations in medical imaging.
Keywords :
Retinal Vessel Segmentation, Deep Learning, Image Processing, Diabetic Retinopathy, Convolutional Neural Networks (CNNs).
