Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study

Abstract

The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients’ effective liver ultrasound images—30 normal, 44 fatty, and 40 cancerous—were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method—75.0%, 0.548, and 0.280—or those of the support vector machine method—75.7%, 0.637, and 0.293—respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

“…Even though not all studies provided information about the methods used to manage data, most studies used cross-validation. Twelve studies implemented 10-fold cross-validation on the testing and training datasets [ 36 , 38 , 39 , 42 , 43 , 52 , 58 , 60 , 62 , 67 , 68 , 70 ], four studies used five-fold cross-validation [ 59 , 63 , 66 , 69 ], one study used four-fold cross-validation [ 77 ], two studies used three-fold cross-validation [ 41 , 54 ], one study used two-fold cross-validation [ 40 ], and one study used one-fold cross-validation [ 74 ]. In addition, four studies mentioned that a leave-one-out cross-validation (LOOCV) method was used [ 55 , 56 , 57 , 79 ].…”

“…One study used three different classifiers: SVM, multi-layered perceptron neural net, and extreme gradient boost [ 70 ]. The other study used two classifiers: LR and SVM [ 68 ]. The rest of the studies used the following models to classify the images: binary logistic regression [BLR][ 71 ], adaptive boosting [ 67 ], Bayes [ 79 ], decision tree [ 41 ], single-layer perceptron network [ 73 ], regression tree model [ 43 ], single-layer feed-forward neural network [ 38 ], ANN [ 80 ], Levenberg–Marquardt back propagation neural network [ 40 ], and z-score [ 66 ].…”

“… The accuracy of NAFLD detection with ultrasonography can be enhanced with the development of computer-aided diagnostic technology, especially for those less trained or operating in distant locations [ 50 , 51 ]. Various image processing techniques will improve image quality, enhancing clinical interpretations and grading performance [ 41 , 53 , 68 , 69 , 78 ]. Some methods provide a fully automated solution that will assist in determining the advantages of telehealth [ 40 , 56 ].…”

“…Various image processing techniques will improve image quality, enhancing clinical interpretations and grading performance [ 41 , 53 , 68 , 69 , 78 ].…”