Team Members: Dr.M.Krishnaveni, Assistant Professor (SG), Department of Computer Science

Ms.S.Aiswarya, Research Scholar, Department of Computer Science

Ms. Vasundra R.S , II M.Sc CS , Department of Computer Science

Project Summary: A Cataract is a cloudy area in the lens of the eyes that leads to a decrease in vision of the eyes. Cataracts often develop slowly and can affect one or both eyes. Visual impairment caused by cataracts is a commonly observed issue and blindness worldwide. Cataracts are one of the visual impairments that can lead to blindness if not detected and treated early. About 20 million people from worldwide are blind due to cataracts. Traditional cataract examination tools and techniques can only be handled by skilled ophthalmologists, making it impractical to conduct mass screenings for early-stage cataract detection due to a shortage of ophthalmologists and the time-consuming nature of these procedures. This project serves as decision support for optometrist and ophthalmologists in identifying cataract types Nuclear Sclerosis, Cortical Cataract, and Posterior Subcapsular Cataract along with their respective grades grade 1, grade 2, grade 3, grade 4. The project involves a systematic approach to developing a deep learning model for cataract multiclassification and grading, aimed at supporting optometrists and  ophthalmologists  in clinical decision-making. Initiall y, Image data are collected from the real-world and case studies to construct a comprehensive dataset. Subsequently, the lens of the eye is segmented using  image processing technique, Image Masking to isolate and focus on the affected areas within the image. Data preprocessing techniques, such as augmentation (flipping, scaling, rotation), are then applied to enhance the diversity of the dataset, which ultimately improves the performance of the deep learning models. For the multiclassification and grading, deep learning models such as CNN, ResNet50, VGG16, and InceptionNet, are employed. These models are trained to classify different types of cataracts (such as Nuclear Sclerosis, Cortical Cataract, and Posterior Subcapsular Cataract) and assign appropriate grades based on the severity of the condition.To further enhance accuracy and robustness, an ensemble learning approach is adopted, where predictions from multiple models (e.g., CNN,VGG16, InceptionV3, ResNet50) are combined using a majority voting classifier. This ensemble learning strategy leverages the strengths of each individual model to generate a final prediction for each image, leading to improved diagnostic accuracy and reliability compared to using individual models. The outcome of this project aims to deliver a sophisticated deep learning model capable of accurate cataract classification and grading and a user-friendly graphical interface to facilitate effective cataract diagnosis and grading by optometrists and ophthalmologists.