## 2 Materials and Methods
### Deep Learning, Kruskal Algorithm and Classifiers  
It is possible to define deep learning as feature extraction, perception, learning, and interpretation of these features by machines. The fact that the features are not extracted by subject matter experts is one of the key characteristics that set deep learning apart from traditional machine learning. In classical machine learning methods, since the features are extracted by experts in the field, it causes time loss and increased cost. Classical machine learning methods cannot process raw data without preprocessing and expert help. With deep learning, this problem of classical machine learning has been eliminated. In other words, in deep learning architectures, features are extracted automatically thanks to the layers used. Also, there is no need to preprocess the data in deep learning architectures [16,17]. Deep learning networks consist of multiple layers that follow each other. The output of each layer is the input of the next layer or layers [18]. The rough flowchart of classical machine learning and deep learning models is presented in Figure 4.  
In this study, CNN architectures, a subbranch of deep learning, were used for automatic feature extraction. CNN architectures are one of the best-known deep learning algorithms and were developed for the computer to process data such as images, audio,  
Figure 4: Flowchart of classical machine learning and deep learning models. Figure 3: Image visualization with the LIME technique. and text. CNN architecture generally consists of one or more convolutional layers, pooling layers, activation functions, and classification layers (Softmax). CNN architectures generally consist of 2 parts. In the first part, the features are extracted. In the second part, classification is made. This structure is shown in Figure 5.  
CNN architecture provides very successful results, especially in areas such as image recognition and classification. In the proposed model, the MobileNet architecture accepted in the literature is used as the base. The MobileNet architecture was developed in 2017. It was stated by [PERSON] et al. that although the performance rates of the developed CNN models increased, satisfactory results were not obtained in terms of speed. For this reason, researchers have tended to develop models with fewer parameters, high performance, and working speed. In this architecture, they aimed to realize mobile and deep learning applications with lower data processing capability. In this architectural feature extraction phase, instead of the classical convolution process, the deep separable convolution (depthwise separable convolutions) technique is used. This method allows for feature extraction with 8-9 times fewer parameters than the conventional convolution procedure [19]. The classification of hydatid cyst CT images was based on the MobileNetV2 architecture. Deep features were obtained from the original dataset with the MobileNetV2 architecture, from the images obtained using Grad-CAM and LIME techniques. The Kruskal algorithm was used to eliminate unnecessary features from the obtained features [20; 21]. Valuable features were selected from feature maps with the Kruskal algorithm. The Kruskal dimension reduction method is a method used to find the important features in the dataset when the data are highly dimensional. In this way, fewer important features are selected while reducing the size of the dataset. These selected features are inclusive enough to describe the dataset. The Kruskal method is frequently used in data mining applications, especially classification and clustering. In addition, Kruskal is frequently used in fields such as image recognition, natural language processing, and biomedical applications. In this study, the proposed model for the classification of hydatid cyst CT images was preferred because it works faster and more effectively. Different classifiers were used to classify the feature map that was optimized using the Kruskal approach.