Liver disease prediction using machine learning

Abstract: This study proposes a deep learning-based approach to classify liver histopathological images into four categories: ballooning, fibrosis, inflammation, and steatosis, using the VGG16 convolutional neural network (CNN). The VGG16 model, pre-trained on ImageNet and fine-tuned on a liver disease dataset, is used for feature extraction and classification. Data augmentation techniques address challenges of limited medical images. The model is evaluated using precision, recall, F1-score, and accuracy metrics. This approach demonstrates the potential of deep learning to support pathologists in diagnosing liver diseases, offering a reliable and automated tool for healthcare professionals.