Glaucoma Detection Using Image Processing and Deep Learning Algorithms

Authors

  • Shreya Pattankede B.M.S College of Engineering, Bangalore
  • Kalpana R. B.M.S College of Engineering, Bangalore
  • Gayathri M. S. B.M.S College of Engineering, Bangalore
  • Sachin Munji B.M.S College of Engineering, Bangalore

Keywords:

CNN (Convolutional Neural Networks), Deep learning, Fundus images, Glaucoma, Image Processing, LSTM (Long short-term memory networks), RNN (Recurrent Neural Network)

Abstract

If glaucoma is not detected and treated in its early stages, it can cause irreversible vision loss. Glaucoma is a degenerative eye condition. The proposed work uses a unique method for glaucoma detection by combining image processing techniques and deep learning methods. Using the characteristics of both domains, the proposed work aims to improve glaucoma detection's accuracy and effectiveness. The first step in our approach involves acquiring high-resolution retinal images, typically obtained through a fundus camera or optical coherence tomography (OCT). These images serve as the input data for subsequent analysis. Image preprocessing techniques enhance image quality, correct artifacts, and improve the contrast of an image, ensuring that the input data is optimal for research. Various image-processing methods strengthen the visibility of pertinent anatomical components, including contrast augmentation, noise reduction, and morphological procedures. High-level features are then extracted from the preprocessed images using a deep-learning architecture. Glaucoma-specific patterns and characteristics are automatically recognized using convolutional neural networks (CNNs). A comprehensive dataset containing standard and glaucoma-affected retinal images is employed to analyze the offered method. The use of the trained deep learning model is evaluated using metrics such as accuracy, sensitivity, precision, and recall. A comparison of the designed method with current glaucoma detection techniques shows that it is accurate and computationally efficient.

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Published

24.03.2024

How to Cite

Pattankede, S. ., R., K. ., M. S., G. ., & Munji, S. . (2024). Glaucoma Detection Using Image Processing and Deep Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 706–712. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5325

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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