Detection of Hepatocellular Carcinoma from B-Mode and Contrast-Enhanced Ultrasound Using a Dual-Path Convolutional Network

BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with particularly severe consequences in sub-Saharan Africa where access to advanced diagnostic imaging remains limited. Ultrasound is the most widely available imaging modality in low-resource settings, yet its sensitivity for detecting early-stage HCC remains insufficient when used in conventional B-mode alone. MethodsWe present a dual-path convolutional neural network (CNN) that jointly analyzes B-mode and contrast-enhanced ultrasound (CEUS) images for automated HCC detection. The model processes 1,057 labeled liver ultrasound images from 85 patients sourced from The Cancer Imaging Archive, a publicly available single-center dataset. A preprocessing pipeline extracts liver-centered regions of interest from heterogeneous DICOM files, including automatic separation of dual-panel B-mode and CEUS frames. Each imaging modality is processed through a dedicated ResNet-34 backbone initialized with ImageNet weights, and the resulting feature embeddings are fused through a late-fusion classification head. The model is evaluated using patient-wise five-fold cross-validation and a held-out 20% patient-level test set. ResultsOn the held-out test set, the model achieved 94.2% accuracy, 93.6% precision, 100% sensitivity, 83.3% specificity, and a 96.7% F1-score for binary HCC versus non-HCC classification. Cross-validation analysis showed consistently high discrimination across folds, with AUC values ranging from 0.93 to 0.98. Training dynamics indicated that early stopping typically activated between epochs seven and eleven, with validation loss closely tracking training loss and no evidence of severe overfitting under the chosen regularization scheme. ConclusionsThese findings demonstrate that a relatively lightweight multimodal CNN, trained on carefully preprocessed public data, can provide strong imaging-level discrimination between HCC and non-HCC findings within a single-center dataset. However, the small sample size, pronounced class imbalance, and single-center origin of the data preclude any claims of clinical utility at this stage. This work is a transparent, reproducible methodological baseline intended to support future multi-site validation, particularly in African and other low-resource clinical settings where ultrasound-based decision support could have the greatest impact.