Advanced Diagnosis of Lens Opacity using Enhanced CNN and Camera Imaging

Since cataracts are one of the main causes of visual impairment in the globe, early identification is essential to successful treatment. Convolutional Neural Networks (CNNs), a deep learning-based method, are used during this study to automatically detect cataracts in digital camera photos. In order to improve accuracy, the suggested model is trained on a collection of ocular pictures using sophisticated image processing techniques. The CNN algorithm successfully recognizes normal & cataract-affected eyes by extracting and evaluating important information. High accuracy in classification in the experimental data highlights the method's promise for accessible, affordable, and non-invasive cataract screening. This method can help with early diagnosis, especially in places with limited resources or distant locations. The work incorporates a number of preprocessing image approaches, including as enhanced contrast, decrease in noise, and data augmenting, to boost the quality and variety of training samples in order to further increase the effectiveness of cataract diagnosis. To get better performance, the structure of the CNN is designed and trained from scratch, and its performance is optimized through hyper parameter tuning. To guarantee dependability in practical situations, the system's resilience is further assessed under various lighting and picture quality settings.