Deep Learning-Enabled Glaucoma Detection Application: Harnessing Amazon Rekognition for Automated Diagnosis

Deep Learning-Enabled Glaucoma Detection Application: Harnessing Amazon Rekognition for Automated Diagnosis

연구 분야: Safety

학회: International Conference on Artificial Intelligence on Textile and Apparel

Advancements in mobile technology and machine learning have enabled innovative solutions in the medical sector, such as glaucoma detection. Glaucoma, caused by optic nerve damage, leads to permanent blindness if not detected early. Our model, trained on a large dataset of labeled fundus images, ensures precise predictions to aid in early detection. The application employs an API Gateway to handle client requests and server responses, while Amazon Rekognition analyzes eye images with a high accuracy score of 94.8. This high accuracy is crucial for reliability in medical applications. AWS Lambda powers the backend, providing scalability to meet varying demands. The solution aims to be user-friendly, making it accessible to the general public. By offering easy access to early glaucoma detection, our application helps promote healthier lives and better patient outcomes. This project underscores the potential of combining advanced machine learning models with robust cloud services to address critical healthcare challenges, ultimately contributing to advancements in ophthalmology and improving overall public health.