System and method to diagnose conjunctivitis in the eye of a user

System and method to diagnose conjunctivitis in the eye of a user

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This Proposed work explores how machine learning can be used to diagnose conjunctivitis, a common eye ailment.The main goal of the study is to capture eye images using camera-based systems, perform image pre-processing, and employ image segmentation techniques, particularly the UNet++ and U-net models.Additionally, sun lite 250w 250v socket the study involves extracting features from the relevant areas within the segmented images and using Convolutional Neural Networks for classification.All this is carried out using TensorFlow, a well-known machine-learning platform.

The research involves thorough training and assessment of both the UNet and U-net++ segmentation models.A comprehensive analysis is conducted, focusing supreme hysteric glamour text on their accuracy and performance.The study goes further to evaluate these models using both the UBIRIS dataset and a custom dataset created for this specific research.The experimental results emphasize a substantial improvement in the quality of segmentation achieved by the U-net++ model, the model achieved an overall accuracy of 97.

07.Furthermore, the UNet++ architecture displays better accuracy in comparison to the traditional U-net model.These outcomes highlight the potential of U-net++ as a valuable advancement in the field of machine learning-based conjunctivitis diagnosis.