A Supervised System Integrating Image Processing and Machine Learning for the Staging of Chronic Hepatic Diseases

Liver disease is a major global health concern. Given the critical role of medical image categorization in fibrosis staging (low, moderate, severe, cirrhotic) and the challenges posed by limited medical image datasets, this paper aims to leverage ultrasound imaging to assess liver margin characteristics at the level of Glisson’s capsule—here referred to as Glisson’s line—to develop a simple, automated model for accurately distinguishing fibrosis stages. The proposed approach combines traditional image processing techniques in a pre-processing stage with machine learning algorithms for classification. The pre-processing phase introduces an attention-focusing mechanism that stretches the gray levels of Glisson’s line while shrinking the intensity levels associated with the liver parenchyma and surrounding tissues. This results in the so-called region of contrast interest (ROCI), where potential classification distractors are minimized. For classification, a convolutional neural network (CNN)-based model is used to process original, rotated, and transformed ultrasound images. To address dataset imbalance and overfitting, a 10-fold cross-validation strategy was implemented. The results demonstrate that, by effectively enhancing the information content of Glisson’s line, different liver fibrosis stages can be accurately distinguished without the need for explicit edge detection, achieving accuracy levels comparable to those reported in the literature. The novelty of this work lies in analyzing the morphology of Glisson’s capsule—obtained through this method—rather than focusing on the liver parenchyma and texture, as is traditionally carried out.