Automated Detection of Liver Steatosis in Ultrasound Images Using Convolutional Neural Networks

The most often used imaging modality for the diagnosis of fatty liver disease is ultrasound imaging. If there is more than 5% fatty hepatorenal steatosis, it is deemed malignant. The traditional procedures for classifying hepatic steatosis mainly need the use of trained radiologists. Methods such as the Hepatorenal Index (HI) and Gray-level co-occurrence matrix (GLCM) are routinely employed. A Convolutional Neural Network (CNN)-based strategy to classifying malignant and benign fatty livers from ultrasound images is presented in this research. The pre-trained Inception Resnet is utilized for transfer learning on B-mode ultrasound liver pictures for categorization. It was first trained on the ImageNet dataset. The softmax activation function is employed as an output layer of the fully connected network to calculate the class-wise probabilities, and the features recovered by the Inception Resnet are provided as input to the fully connected network. We used a 550-image open-source ultrasound liver dataset comprising 170 normal and 380 malignant samples. Our tests revealed that the Inception Resnet has a classification accuracy of 98.48%, whereas the HI and the GLCM methods have accuracies of 90.9 and 85.4 percent, respectively.