# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 3 Materials and Methods
### Methodology
#### 3.2.2 Classification
When no disease was found, True Negatives (TN) were observed, and the test was

Figure 5: (**a**) Ultrasonic image (Original). (**b**) The transformed US image by using Equation (1). (**c**) The selected grey ROIs. Figure 6: The change in penalty values due to generation numbers. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{(**a**) Classification percentages for the training images.} \\ \hline
**Case** & **True Images** & **False Images** & **Total** \\ \hline Training images & 228 & 2 & 230 \\ Accuracy & 99.13\% & 0.87\% & 100\% \\  & \multicolumn{3}{c}{(**b**) Classification calculations for the testing US images.} \\ \hline
**Case** & **True Images** & **False Images** & **Total** \\ \hline Testing images & 67 & 3 & 70 \\ Accuracy & 95.71\% & 4.29\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification percentages of the liver images. negative, while it was classified as false positive (FP) if the test was positive. Instead, if the disease was present, a positive or negative test indicated the observations were classified as True Positive (TP) or False Negative (FN) situations, respectively [17; 48; 49]. Subsequent performance measures are displayed in Table 4.

Table 5 presents the robust widely used performance measures: _sensitivity, accuracy, precision, specificity_, and _F1-score. Accuracy_ is an intuitive measure of _accuracy_ and is simply the ratio of properly expected observations to total observations. _Precision_ is a calculation of correctly expected real observations to total predicted real observations. The _sensitivity_ (recall) is a ratio of the relevant instances that were regained. _Specificity_ is a proportion of healthy persons who were correctly reported as not having the illness. Finally, the _F1-Score_ is the average of sensitivity and precision. Consequently, the aforementioned score takes both _FP_ and _FN_ into account [48; 49] These performance metrics are known by:

\[Accuracy=\frac{(TN+TP)}{(TN+TP+FN+FP)} \tag{2}\]

\[Precision=\frac{TP}{(FP+TP)} \tag{3}\]

\[Sensitivity=\frac{TP}{(FN+TP)} \tag{4}\]

\[Specificity=\frac{TN}{(FP+TN)} \tag{5}\]

\[F_{1-measure}=2*\frac{(Sensitivity*Precision)}{(Sensitivity+Precision)} \tag{6}\]

Table 6 introduces a comparison of the recognition rate of our proposed technique with that in [18; 23; 26; 50]. Our proposed technique produced more accurate results than those in the referred references. The suggested method achieved better accuracy compared to other research. It should be noted that the work by other researchers used an ultrasound imaging system, which was better than the proposed image acquisition system. Our acquisition system was a very popular product in the field of medical equipment, because it was so inexpensive compared to other acquisition systems. The range of the transmitting frequency \begin{table}
\begin{tabular}{c c} \hline \hline  & **Case: Normal/Fatty (70 Test Images) 35** \\  & **Normal and 35 Fatty** \\ \hline TP & 33 (94.28\%) \\ TN & 34 (97.14\%) \\ FP & 2 (5.71\%) \\ FN & 1 (2.85\%) \\ Total & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The values of TN, TP, FP, and FN. \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Metrics** & **Percentage** \\ \hline _Accuracy_ & 95.71\% \\ \hline _Precision_ & 94.28\% \\ \hline _Sensitivity_ & 97.05\% \\ \hline _Specificity_ & 94.44\% \\ \hline _F1-score_ & 95.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of some performance metrics. of the ultrasound in our device was the same with other devices. The resolution of 684 \(\times\) 552 pixels in our device was lower than the resolution of other devices, e.g., 767 \(\times\) 572 pixels and 1024 \(\times\) 1024 pixels. Moreover, the research work by [18; 23; 26; 50] used a greater number of extracted features, which required high computation complexity. The use of a smaller number of features allowed us to avoid the use of GPU as in [51; 52].