Enhancing Fatty Liver Disease Diagnosis Using Deep from Ultrasound Images

The increasing prevalence of fatty liver disease necessitates accurate and efficient diagnostic methods. This study investigates the integration of deep learning techniques to enhance the diagnosis of fatty liver disease using ultrasound images. A dataset was utilized to train a deep learning model. The model achieved an impressive accuracy of 96% on the training dataset for distinguishing patients with fatty liver disease from healthy individuals, while maintaining a commendable accuracy of 90% on a completely new dataset. Furthermore, the model demonstrated a sensitivity of 92% in classifying different levels of liver fat in the training dataset, with an accuracy of 83% on the new dataset. These results underscore the effectiveness of combining deep learning in medical imaging, providing a robust framework for the early detection and classification of fatty liver disease. The findings suggest that this hybrid approach can significantly improve diagnostic accuracy, ultimately contributing to better patient management and outcomes in clinical practice. Further research is warranted to validate these results across diverse populations and enhance model generalizability.