Cataract Diseases Prediction Using Deep Learning

Abstract

This research focuses on cataract disease diagnosis and prediction using machine learning (ML) models. It seeks to solve the difficulties associated with predicting cataract illness by fusing medical necessity with technology breakthroughs. The complex problem statement includes issues including the lack of data, the lack of resources, and the requirement for preventative healthcare actions. By recognizing and addressing these issues, the article seeks to establish the groundwork for a strong and reliable cataract prediction paradigm. The study investigates the use of several machine learning models, such as ensemble approaches, decision trees, and neural networks, and assesses how well they predict the occurrence of cataracts. This methodology advances cataract prediction while also providing a more comprehensive investigation of machine learning applications in healthcare. The application of several convolutional neural network (CNN) models and their analysis. It explores the technical details of the models, such as padding, normalization, kernel sizes, and filter counts. By fusing technology breakthroughs with healthcare requirements, the project aims to contribute to the changing field of healthcare innovation by providing a more nuanced understanding of cataract prevalence and a route toward more effective, focused, and compassionate healthcare interventions. The paper discusses the effectiveness of several convolutional neural network (CNN) models for classifying cataract eye images. A comparison is made between the accuracy of multiple models: KNN, Inception V3, Xception, LeNet-CNN, and the proposed SqueezeNet model. This work emphasizes how important it is to choose the right CNN architecture for cataract eye classification and highlights the possibility for improving diagnostic capabilities in clinical situations. The suggested SqueezeNet model achieved the best classification accuracy for cataract eye pictures, demonstrating its applicability in this specific use case. This highlights the importance of choosing the right CNN architecture for cataract eye categorization as well as the possibility of improving diagnostic capabilities in clinical situations