Published Paper Details:

Cataracts are a common eye condition that can lead to vision loss if not detected early. Traditional diagnosis methods rely on manual examination by eye specialists, which can be time-consuming and subjective. This study explores the use of RESNET-152, a deep learning-based Convolutional Neural Network (CNN), to detect cataracts from eye images automatically. The model is trained on a dataset of fundus images and can classify eyes as normal or cataract affected. By using deep learning, the system provides a fast, accurate, and reliable method for cataract detection, which can assist doctors in early diagnosis and treatment. Traditional cataract diagnosis relies on manual examination by ophthalmologists, which can be time-consuming and subjective. In this study, we propose an automated cataract detection system using the RESNET-152 convolutional neural network (CNN) for feature extraction and classification. The model is trained on a dataset of fundus images, where it learns to distinguish between normal and cataract-affected eyes with high accuracy. To address these challenges, this study proposes an automated cataract detection system using RESNET-152, a deep learning-based Convolutional Neural Network (CNN). The model is trained on a dataset of fundus images, learning to extract important visual features that differentiate normal and cataract affected eyes. Transfer learning is employed to enhance model efficiency, leveraging pre-trained weights from large-scale image datasets. The deep learning-based approach leverages transfer learning to enhance feature representation, ensuring robust detection even in challenging cases.