## 4 Discussion  
Hydatid cyst disease is an infection caused by the Echinococcus granulosa parasite, which is endemic in many countries. The disease causes cyst formation in many organs of the body. It is a common disease, especially in small cattle-breeding countries, including our country. It is transmitted to humans by ingesting egg-contaminated food from hosts such as dogs, foxes, and sheep. In adults, the cyst most often settles in the liver. Imaging methods such as ultrasonography (USG), computed tomography (CT), and magnetic resonance (MRI) are used for diagnosis [29; 30]. CT shows the presence of calcification in the cyst. It is also used in the evaluation of complications. Intrahepatic and extrahepatic complications of hydatid cyst disease can be seen. Intrahepatic complications are infection, biliary tract opening, and vascular findings. Extrahepatic complications are hematogenous spread to distant organs and peritoneal rupture [31; 32]. Hydatid cyst is a disease with high morbidity with cyst formation and complications in many organs in the body. Radiological imaging has an important role in the diagnosis, treatment, and follow-up of this disease. With imaging methods, it is determined whether the cyst is active or inactive, and the treatment steps are decided. Hydatid cyst infection is a serious health problem, and early diagnosis and treatment are important. Early detection can reduce the growth of cysts and damage to surrounding tissues and reduce the risk of further infections. It can also help prevent serious complications that can have fatal consequences if left untreated. Symptoms of hydatid cyst infection are usually very mild and may go unnoticed in the early stages of the disease [33; 34]. Therefore, in this study, first of all, the image visualization process was carried out so that the hydatid cyst CT images can be interpreted more easily and error-free by experts. Then, hydatid cyst CT images were classified. The model developed in the study obtained better results than the pretrained models accepted in the literature and similar studies in the literature. There are similar studies in the literature for the classification of hydatid cyst CT images. Studies on the classification of hydatid cyst CT images are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Year** & **Method** & **Number of Patients** & **Accuracy** \\ \hline [PERSON] et al. [9] & 2022 & VGG19, InceptionV3, ResNet18 & 967 & 90.6\% \\ \hline [PERSON] et al. [10] & 2019 & Conventional machine learning, deep learning & - & - \\ \hline [PERSON] et al. [11] & 2020 & CNN & 160 & 82.45\% \\ \hline [PERSON] et al. [8] & 2023 & CNN, INCA & 2416 & 92.0\% \\ \hline Proposed model & 2023 & MobileNetV2, Grad-CAM, LIME, classifiers & 2416 & 94.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Literature review on hydatid cyst CT images. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** & **FPR** & **FDR** & **FNR** & **F1** \\ \hline
**Class 1** & 94 & 92.15 & 99.30 & 0.69 & 6 & 7.84 & 93.06 \\ \hline
**Class 2** & 95.37 & 95.37 & 98.66 & 1.33 & 4.62 & 4.62 & 95.37 \\ \hline
**Class 3** & 95.50 & 95.50 & 98.98 & 1.01 & 4.49 & 4.49 & 95.50 \\ \hline
**Class 4** & 93.18 & 88.17 & 98.46 & 1.53 & 6.81 & 11.82 & 90.60 \\ \hline
**Class 5** & 97.88 & 93.91 & 99.10 & 0.89 & 2.11 & 6.08 & 95.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance values of the proposed hydatid cyst model (%). When similar studies in the literature for the classification of hydatid cyst CT images are examined, it is seen that the proposed model is successful. The accuracy rates we obtained in this study are for the classification of liver hydatid disease, and additional studies are needed for its application for cysts in different localizations in the body. The biggest limitation of this study is the collection of data from a single center.