A Novel Method for Viral Conjunctivitis Detection using CNN-Based Image Analysis

Abstract

Viral conjunctivitis, also known as “Eye Flu,” presents significant public health challenges worldwide. India has recently witnessed a surge in cases, affecting numerous people and causing widespread concerns. This research delves into the realm of medical image processing and deep learning to address the pressing need for accurate and efficient detection of viral conjunctivitis, a highly contagious ocular infection. Leveraging advancements in computer vision and convolutional neural networks (CNNs), the study focuses on the development and evaluation of a robust diagnostic system capable of discerning viral conjunctivitis from other common forms of conjunctivitis, namely allergic and bacterial.

Challenges in using image processing for disease detection include the need for large amounts of descriptive data to train machine learning models, ensuring the accuracy and reliability of image analysis algorithms, and addressing data privacy and security concerns. Future directions in this field may include developing more advanced deep learning models that can handle complex medical imaging data, integration of imaging technology and other diagnostic tools to diagnose diseases and learn how to use it in real-time for fast processing. and more efficient diagnosis. Additionally, efforts should be made to standardize image processing protocols in different healthcare settings to facilitate sharing and comparison of medical imaging data for research and clinical purposes. Overall, more research and collaboration between medical, informatics, and image processing experts is essential in developing future image processing applications for disease detection.

The methodology encompasses the acquisition of a diverse dataset comprising annotated images of ocular conditions, rigorous preprocessing techniques to standardize image quality, and the implementation of three distinct CNN architectures: ResNet, VGG, and GoogleNet. These architectures were selected for their proven efficacy in medical image analysis and classification tasks. Through extensive experimentation and rigorous validation, the research elucidates the efficacy of each architecture in accurately classifying conjunctival diseases, with a particular emphasis on delivering actionable diagnostic outcomes.