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

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

Blog Article

This Proposed work explores how machine learning can be used to diagnose conjunctivitis, a tenga flip orb 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, 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 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 here 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.