# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 2 Background  
FLD may progress to advanced liver diseases, such as hepatic transaminases, cryptogenic hepatocellular disease, and cirrhosis, which may eventually result in death [5; 6]. Table 1 shows the ultrasound (US) findings and specific causes of liver disease. Despite the accurate results of liver biopsy for FLD detection and diagnosis; however, it has certain associated weaknesses. Those weaknesses consist of bleeding until death, penetration of the skin, and some complications, e.g., increased abdominal pain, chills, dizziness, difficulty of breathing, abdominal pain, and chest, fever, and redness or severe pain at the position of the biopsy [7; 8; 9; 10]. Different noninvasive methods are used to reduce unnecessary liver biopsy cases and to observe FLD, mainly imaging techniques, such as ultrasound imaging analysis. Ultrasound is preferred by radiologists because it is nonexpensive, noninvasive, easy to operate, and radiation-free. In addition, it provides higher average specificity and sensitivity for detecting fatty liver when compared to other kinds of noninvasive imaging techniques such as (1) Magnetic Resonance (MR) imaging and (2) Computed Tomography (CT). CT is limited by the standardization required for the different scanners and the inter-observer variability. In radiomics [11; 12], the standardization of feature extraction methodology is significant for second and higher-order texture features. Furthermore, MR techniques can be technically challenging. A functional MR of the liver diagnosis requires stability adequate for the elimination of patient motion [13; 14; 15; 16]; a brief description of these diagnosis approaches is shown in Table 2.  
One of the major limitations of using ultrasound is the low quality of the acquired images compared to those of MRI and CT. This means that the classification task is extremely difficult and is one of the challenges addressed in this research. One method to reduce the limitations of an ultrasound is to improve a Computer-Aided Diagnosis (CAD) system. CAD offers a promising solution to reduce variation in colonoscopy performance [17]. Many of the CAD systems have been developed using \begin{table}
\begin{tabular}{c c c} \hline \hline
**Diseases** & **Causes** & **Ultrasound Findings** \\ \hline \multirow{3}{*}{Fatty liver} & Drug misuse, contaminants, metabolic illness, and obesity & Fine parenchymal texture, decreased number of vessels, hepatomegaly, and increased echogenicity \\ \hline \multirow{3}{*}{Hepatitis} & Infections from & Diffusively decreased echogenicity and hepatomegaly \\ \cline{2-3}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{3}{*}{Fibrosis} & Hepatic venous obstruction, chronic hepatitis, metabolic disorder, prolonged cholesterasis, and immune disorder & Healthy appearance of liver, a slight increase in echogenicity, and coarse echo-texture \\ \hline \multirow{3}{*}{Cirrhosis} & & Shrunken liver, rounded contours, small right lobe with enlarged left and caudate lobes, (volume redistribution), regenerative nodules, and modularity resulted in portal hypertension manifestations of surface irregularity. \\ \cline{1-1}  & & Decreased number of vessels. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main types of diffused liver diseases and ultrasound findings. medical liver images for the diagnosis of liver diseases [18; 19]. The usage of a CAD method reduces the user's intervention and helps experts and physicians to detect FLD through analyzing ultrasound images. The CAD introduced by [PERSON] et al. [20], developed a procedure to make nonsteatosis (healthy liver) distinguished. Nonsteatosis and steatosis (fatty) specials of the liver are imaged by ultrasonography of high frequency. The authors retrieved and used two characteristics, Support Vector Machine (SVM), as a classifier to obtain an accuracy of up to 90%. A CAD system was developed by [PERSON] et al.