Multiscale Feature Fusion Method for Liver Cirrhosis Classification

Liver cirrhosis is one of the most common liver diseases in the world, posing a threat to people's daily lives. In advanced stages, cirrhosis can lead to severe symptoms and complications, making early detection and treatment crucial. This study aims to address this critical healthcare challenge by improving the accuracy of liver cirrhosis classification using ultrasound imaging, thereby assisting medical professionals in early diagnosis and intervention. This article proposes a new multiscale feature fusion network model (MSFNet), which uses the feature extraction module to capture multiscale features from ultrasound images. This approach enables the neural network to utilize richer information to accurately classify the stage of cirrhosis. In addition, a new loss function is proposed to solve the class imbalance problem in medical datasets, which makes the model pay more attention to the samples that are difficult to classify and improves the performance of the model. The effectiveness of the proposed MSFNet was evaluated using ultrasound images from 61 subjects. Experimental results demonstrate that our method achieves high classification accuracy, with 98.08% on convex array datasets and 97.60% on linear array datasets. Our proposed method can classify early, middle, and late cirrhosis very accurately. It provides valuable insights for the clinical treatment of liver cirrhosis and may be helpful for the rehabilitation of patients.