Predicting Liver Disease from MRI with Machine Learning-Based Feature Extraction and Classification Algorithms

Globally, liver disease is the leading cause of death for a huge number of people. Inflammation of the liver is caused by a number of factors. Diagnosing liver infection early is essential for more effective treatment. In the current scenario, sensors are employed to identify liver diseases. Precise classification methods are necessary for the automatic diagnosis of illness samples. The cost of diagnosing this illness is high and complicated. The purpose of this study is to decrease the high cost of chronic liver disease diagnosis through prediction. This paper reviews the emerging techniques of data pre-processing, feature extraction, and classification on liver MRI. The primary goal of the current work is to use clinical data to predict the presence or absence of liver disease from MRI by applying various Machine Learning methods. In this paper, we have performed feature extraction from liver MRI using the HOG method followed by the Random Forest algorithm for the classification of images. With our approach, the accuracy achieved is 91.67%.