USLF-Net: A Task-Specific CNN for Liver Fibrosis Classification from Ultrasound with Mobile Clinical Integration

Accurate liver fibrosis classification is crucial for makingtreatment decisions in patients with chronic liver disease. Liver biopsyand advanced noninvasive imaging modalities, including elastography,computed tomography (CT), and magnetic resonance imaging (MRI),remain challenges in resource-limited settings due to their high cost andspecialized equipment requirements. Consequently, conventional B-modeultrasound is widely available and cost-effective. Despite the considerablepromise of deep learning in medical imaging, prior works often have highcomputational demands, which limit their practicality. In this study, weintroduce USLF-Net, a novel UltraSound-Based Liver Fibrosis Clas-sification using Convolutional Neural Network (CNN). Our model wasvalidated using a comprehensive dataset comprising 6,323 ultrasound im-ages, stratified across five fibrosis stages (F0-F4) according to the Meta-analysis of Histological Data in Viral Hepatitis (METAVIR) scoring sys-tem. USLF-Net achieved 97.64% classification accuracy, demonstratingsuperior performance compared to recent transfer learning approaches.We further developed a clinical application for automated liver fibrosisdiagnosis to enhance diagnostic capabilities in healthcare settings