Applied Computer Vision through Artificial Intelligence

3From Pixels to Predictions: Deep Learning for Glaucoma Detection

Tushar Verma¹*, Sachin Ahuja¹ and Jasminder Kaur Sandhu²

¹Department of Computer Science and Engineering, Chandigarh University, Mohali, India

²School of Computer Science and Engineering, IILM University, Greater Noida, India

Abstract

Providing a performance study of a Deep Unified Model for glaucoma detection is an important undertaking that involves using hybrid models, such as ResNet and DenseNet, inside a CNN-based image classification framework. The research addresses the critical need for early detection of glaucoma, a leading cause of blindness. Leveraging the power of deep learning, particularly CNNs, this study explores the transformative potential of pixel-level analysis in ophthalmic imaging. We delve into the fundamentals of ophthalmic imaging, discussing the challenges of glaucoma detection and the limitations of traditional diagnostic methods. Through the lens of deep learning, we propose a comprehensive approach to designing models for glaucoma detection, emphasizing the significance of interpretable and clinically relevant outcomes. The study encompasses the entire pipeline from preprocessing ophthalmic data to training and testing deep learning models, ensuring a meticulous examination of performance metrics and interpretability. Case studies showcase real-world applications and the impact on patient outcomes, while ethical considerations underscore the responsible integration of AI into healthcare. ...