## 1 Introduction  
The Echinococcus granulosus worm can infect cysts, leading to a condition known as a hydatid cyst. This infection is caused by food and water contaminated with fecal material released by dogs and other carnivorous animals. This infection also occurs in other mammals [1]. A hydatid cyst is a fluid-filled sac caused by a single-celled parasite. These cysts usually show no symptoms and can grow for years without showing symptoms. However, large cysts may reduce the patient's quality of life by pressing on the surrounding tissues, or they may cause serious health problems as a result of the eruption of the cyst [2,3]. For diagnosis, X-ray, ultrasound, or magnetic resonance imaging tests are usually conducted after a physical exam. Blood tests can also be helpful for diagnosis. Treatment usually depends on the location of the cyst, the patient's general health, and the size of the cyst [4,5]. Hydatid cyst treatment may vary depending on the size of the cyst and the location of the cyst. Small cysts can be treated with medication, while large cysts usually require surgery. Medication can often be used before or after surgical treatment. The most common treatment is to remove the tumor, but sometimes medication is also recommended. Hydatid cysts can be prevented by preventing infection. It is possible to prevent the spread of infection through good hygiene habits and using enough hot water when cooking meat [6,7]. In conclusion, hydatid cysts are a common type of parasitic infection that can cause serious health problems. However, medical treatment is available and early diagnosis and treatment are important. To reduce the misclassification rates of these cysts, the image visualization and classification process is emphasized. The classification of hydatid cyst types has been studied in some of the literature. [PERSON] et al. carried out a classification of hydatid cyst images, initially conducting feature extraction from their dataset. The INCA (iterative neighborhood component analysis) method was used to eliminate unnecessary features from these extracted features. Then, they classified the feature map optimized using the INCA method in classical classifiers. The model the researchers used in the study had a 92% accuracy rate [8]. In this study, the dataset shared by [PERSON] et al. was used. In the study of [PERSON] et al., the image visualization step was not included. In this study, image visualization was performed with gradient-weighted class activation mapping (Grad-CAM) and locally interpretable model-agnostic explanation (LIME) techniques. [PERSON] et al. aimed to classify five different hepatic cystic echinococcosis subtypes based on deep learning algorithms using ultrasound images. Researchers used pretrained VGG19, Inception-v3, and ResNet18 models in their study. The study used a total of 1820 abdominal ultrasound pictures from 967 patients, representing five subtypes of hepatic cystic echinococcosis. In pretrained models, accuracy rates between 88.2% and 90.6% were attained. In the investigation, the researchers' highest accuracy result for the VGG19 architecture was 90.6% [9]. To categorize CT images of liver hydatid cysts, [PERSON] et al. employed and contrasted both conventional machine learning techniques and deep learning techniques. In the process of classification of CT images, firstly, the preprocessing step was carried out. Median filtering was used in this step. After feature extraction, decision trees and a support vector machine (SVM) were used for classification. Researchers have stated that the SVM outperforms the decision tree for CT images of hepatic hydatid disease [10]. [PERSON] et al. stated in their study that hepatic echinococcosis is a liver disease that requires a definitive diagnosis and appropriate treatment. To reliably analyze lesions of hepatic echinococcosis, they suggested a new automated lesion segmentation and classification network that contains modules for lesion site location and lesion site segmentation.