Towards Accurate Detection of Diabetic Retinopathy Using Image Processing and Deep Learning

Diabetic retinopathy (DR) is a critical complication of diabetes, characterized by pathological changes in retinal blood vessels. This paper presents an innovative software application designed for DR detection and staging using fundus images. The system generates comprehensive reports, facilitating treatment planning and improving patient outcomes. Our study aims to develop an affordable computer assisted analysis system for accurate DR assessment, leveraging publicly available fundus image datasets. Key objectives include identifying relevant features for DR staging, developing robust image processing algorithms for lesion detection, and implementing machine learning models for accurate diagnosis. The research employs various pre-processing techniques to enhance image quality and optimize feature extraction. Convolutional Neural Networks (CNNs) are utilized for stage classification, achieving an impressive accuracy of 93.45%. Lesion detection algorithms, including optic disk localization, blood vessel segmentation, and exudate identification, demonstrate promising results in accurately identifying DR-related abnormalities. The developed software product integrates these advancements, providing a user-friendly interface for efficient DR diagnosis and management. Evaluation results validate the effectiveness of the CNN model in stage classification and lesion detection, with high sensitivity and specificity. The study discusses the significance of image augmentation and hyperparameter tuning in improving model performance. Future research directions include enhancing the detection of microaneurysms and hemorrhages, incorporating higher resolution images, and standardizing evaluation methods for lesion detection algorithms. In conclusion, this research underscores the potential of technology in revolutionizing DR diagnosis and management. The developed software product offers a cost-effective solution for early DR detection, emphasizing the importance of accessible healthcare solutions. The findings contribute to advancing the field of DR analysis and inspire further innovation for improved patient care.

@article{Silva2024,
  title     = {Towards Accurate Detection of Diabetic Retinopathy Using Image Processing and Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {K. Kalindhu Navanjana De Silva and T. Sanduni Kumari Lanka Fernando and L. D. Lakshan Sandaruwan Jayasinghe and M.H.Dinuka Sandaruwan Jayalath and Kasun Karunanayake and B.A.P. Madhuwantha},
  doi       = {10.14569/IJACSA.2024.0150986},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150986}
}