Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning
Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness,...
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221501612400503X |
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| author | Govindharaj I Poongodai A Gnanajeyaraman Rajaram Santhakumar D Ravichandran S Vijaya Prabhu R Udayakumar K Yazhinian S |
| author_facet | Govindharaj I Poongodai A Gnanajeyaraman Rajaram Santhakumar D Ravichandran S Vijaya Prabhu R Udayakumar K Yazhinian S |
| author_sort | Govindharaj I |
| collection | DOAJ |
| description | Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection. This research explores a hybrid approach combining UNet++ and Capsule Network (CapsNet) architectures for accurate glaucoma diagnosis. UNet++ is employed for semantic segmentation, focusing on defining optic discs and cups, which are crucial for detecting the disease. CapsNet leverages its ability to recognize hierarchical patterns, providing more sensitive detection of glaucomatous changes than conventional Convolutional Neural Networks. Pre-processing of retinal images involves advanced techniques like Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. The model is trained and tested on benchmark datasets, showing superior performance in optic cup/disc segmentation and glaucoma detection accuracy compared to existing state-of-the-art models. • Hybrid Model Efficiency: The combined use of UNet++ and CapsNet offers improved accuracy in optic cup and disc segmentation. • Enhanced Image Quality: Application of Histogram Equalization and CLAHE techniques significantly boosts the quality of retinal images. • Superior Performance: The hybrid approach outperforms traditional and contemporary models in glaucoma detection accuracy. |
| format | Article |
| id | doaj-art-1fd9bb0cf3304424b9a25ded36e133d4 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-1fd9bb0cf3304424b9a25ded36e133d42024-12-18T08:48:59ZengElsevierMethodsX2215-01612025-06-0114103052Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learningGovindharaj I0Poongodai A1Gnanajeyaraman Rajaram2Santhakumar D3Ravichandran S4Vijaya Prabhu R5Udayakumar K6Yazhinian S7Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, India; Corresponding author.Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence), Madanapalle Institute of Technology & Science, Andhra Pradesh, 517325, IndiaProfessor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Tamil Nadu, 602105, IndiaProfessor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Tamil Nadu, 602105, IndiaProfessor, Department of Artificial Intelligence and Machine Learning, Kings Engineering College, Tamil Nadu, 602117, IndiaAssistant Professor, Department of Information Technology, Sri Manakula Vinayagar Engineering College, Puducherry, 605107, IndiaAssistant Professor (Sr.Grade), Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, IndiaAssistant Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, IndiaGlaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection. This research explores a hybrid approach combining UNet++ and Capsule Network (CapsNet) architectures for accurate glaucoma diagnosis. UNet++ is employed for semantic segmentation, focusing on defining optic discs and cups, which are crucial for detecting the disease. CapsNet leverages its ability to recognize hierarchical patterns, providing more sensitive detection of glaucomatous changes than conventional Convolutional Neural Networks. Pre-processing of retinal images involves advanced techniques like Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. The model is trained and tested on benchmark datasets, showing superior performance in optic cup/disc segmentation and glaucoma detection accuracy compared to existing state-of-the-art models. • Hybrid Model Efficiency: The combined use of UNet++ and CapsNet offers improved accuracy in optic cup and disc segmentation. • Enhanced Image Quality: Application of Histogram Equalization and CLAHE techniques significantly boosts the quality of retinal images. • Superior Performance: The hybrid approach outperforms traditional and contemporary models in glaucoma detection accuracy.http://www.sciencedirect.com/science/article/pii/S221501612400503XHybrid UNet++-CapsNet Framework for Automated Glaucoma Detection |
| spellingShingle | Govindharaj I Poongodai A Gnanajeyaraman Rajaram Santhakumar D Ravichandran S Vijaya Prabhu R Udayakumar K Yazhinian S Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning MethodsX Hybrid UNet++-CapsNet Framework for Automated Glaucoma Detection |
| title | Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning |
| title_full | Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning |
| title_fullStr | Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning |
| title_full_unstemmed | Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning |
| title_short | Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning |
| title_sort | enhanced diabetic retinopathy detection using u shaped network and capsule network driven deep learning |
| topic | Hybrid UNet++-CapsNet Framework for Automated Glaucoma Detection |
| url | http://www.sciencedirect.com/science/article/pii/S221501612400503X |
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