A Liver Cirrhosis Reorganization in Medical Images Using Enhanced ResNet-50 with Relu6 Activation Function

This study offers new insights into the management of liver cirrhosis, serving as a vital resource for public health practitioners. The findings emphasize the importance of evidence-based treatments and enhanced collaboration among researchers, health care professionals, and other stakeholders to reduce the incidence, morbidity, and mortality associated with liver cirrhosis. A novel approach utilizing deep learning models, particularly the ResNet50 model, which achieves a remarkable accuracy, is proposed for the improved detection of liver fibrosis. This high diagnostic accuracy is facilitated by effective feature extraction and optimization through back propagation in machine learning models. Additionally, the integration of XGBoost, a robust machine learning algorithm known for its speed and efficiency, further enhances patient classification and predictive modeling. This combined approach holds the potential to transform clinical outcomes by providing a more accurate and effective diagnostic and therapeutic strategy for liver fibrosis, paving the way for more personalized medical interventions.