EFFECTIVE MODELS FOR DETECTION OF VIRAL HEPATITIS B BY COMPUTER VISION

Abstract

Viral hepatitis B is a significant global health concern, affecting millions of individuals worldwide. Timely and accurate detection of hepatitis B is crucial for effective disease management and prevention of its complications. In recent years, computer vision techniques have emerged as powerful tools for medical diagnostics, offering automated and objective analysis of medical images. This article explores the effectiveness of various computer vision models in the detection of viral hepatitis B. Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in medical image analysis tasks. By training CNNs on liver images obtained from ultrasound, CT scans, or MRI scans, specific patterns and features associated with hepatitis B can be identified. Transfer learning with pre-trained models, such as VGG, ResNet, or Inception, enhances detection performance by leveraging learned features from general image recognition tasks. Image segmentation models, including U-Net, Mask R-CNN, and FCN, play a pivotal role in localizing and identifying affected areas within liver images. These models enable precise identification of hepatitis B-related lesions and provide crucial information for accurate diagnosis. Ensemble models, which combine predictions from multiple individual models, further improve the overall detection performance. This article discusses the architecture, training process, and performance evaluation of these computer vision models.