# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 2 Background
[21] for classifying steatosis and nonsteatosis using Radio Frequency (RF), the signal made by the ultrasound examination was employed to extract only two images. The first one is a speckle-only image, and the second is a despeckled image with descriptions and anatomical characteristics. The intensity features were calculated from the despeckled image and the speckle image, associated with two texture features extracted. The classification process was performed on two categories, ten steatosis liver images and ten nonsteatosis ultrasonic liver images; the Bayes classifier was used in this study and obtained an accuracy of 95%. [PERSON] et al. implement the CAD system in [22], which specialists combined to analyze and diagnose liver focal lesions from 2D-mode ultrasound images. The authors used four different types of focal liver lesions and associated them with the normal human liver. In the ultrasound image, the noise was decreased by filtration, and then they divided the ROIs into 800 segmented sections. Afterward, based on the texture, near 208 texture-based features were extracted from separate ROI. The achieved accuracy of the use of the \begin{table}
\begin{tabular}{c c c} \hline
**Diagnosis Methods** & \
\begin{tabular}{c} **Weaknesses and Drawbacks** \\ **of These Methods** \\ \end{tabular} &
\begin{tabular}{c} **Drawbacks of Those** \\ **Methods** \\ \end{tabular} \\ \hline \multirow{4}{*}{Liver Biopsy} & Examination with prognostic value. & It is a technique of invasiveness. \\  & Links with the level of liver injury. & Complications such as bleeding and discomfort are likely. \\  & & Operator dependent. \\ \hline \multirow{4}{*}{Blood tests} & The leading supporter in the assessment of essential liver function. & Low specificity. \\  & High sensitivity with improved standards of ALT and AST. & No relationship with the level of diffused liver tissue injury. \\ \hline \multirow{4}{*}{MRI} & Possibility of analysis of spectroscopy. & The high expense of the test. \\  & Proper quantification of the fat content of the human liver. & The fat quantification \\  & & mistakes (or inaccuracies) in \\  & & the presence of high iron \\  & & Proper quantification of the \\  & fat content of the human liver. & Unsuitable for patients with \\  & & planted electronic devices, for \\  & & example, pacemakers. \\ \hline \multirow{4}{*}{CT} & Characterize fatty or & Low sensitivity for early-stage \\  & (steatosis) by the lower liver & fatty (or steatosis). \\  & intensity. & Use of ionizing radiation. \\  & Quantitative measurement. & Interequipment variability. \\ \hline \multirow{4}{*}{Ultrasound} & Advised for initial diagnosis. & \multirow{4}{*}{Inappropriate for cases with high Body Mass Index (BMI).} \\  & The high specificity of fat. & \\ \cline{1-1}  & Accumulation is greater than & \\ \cline{1-1}  & 33\%. & Intraoperator variability. \\ \cline{1-1}  & Effective, low cost, and & Operator dependent. \\ \cline{1-1}  & noninvasive. & \\ \hline \end{tabular}
\end{table} 
Table 2: Invasive and noninvasive test methodology for evaluation of FLD overview. ANN approach was about 86%. [PERSON] et al. [23] suggested a classification manner to discriminate steatosis and a normal human liver. In this study, the authors extracted a combination of image texture features from the diffused liver. Applying this classification technique, the authors obtained an accuracy of about 93%. [PERSON] et al. proposed a CAD system in [24] for FLD analysis and classification. In total, 36 features were extracted from each selected ROI from the ultrasound image. Then, based on three different classifiers: k-Nearest Neighbor (k-NN), Bayes, and Support Vector Machine (SVM), the obtained accuracy was recorded at 85.71%. [PERSON] et al. [25], utilizing an SVM classifier based on liver ultrasound images. The extracted features were from GLCM, Near-Far-Field Greyscale Ratio (NFFGR), and Neighborhood Grey-Tone Difference Matrix (NGTDM). As a consequence of using a previous technique, the total accuracy recorded was 90.55%. [PERSON] et al.