###### Abstract
There are ways to circumvent this problem, such as using data augmentation, transfer learning, or GAN networks, which in the medical community still imply the question of whether these artificial intelligence methods can provide sufficiently reliable methods that can be used in CAD. Instead, an expert system based on medical knowledge can be constructed, which can be made more accurate with the data of certain available samples. Fuzzy systems [7], or fuzzy signatures are especially suitable for creating such solutions. Such systems perform surprisingly well in fields with data that can be collected from a lot of work, e.g., in civil engineering, where earthquake resistance [8] or structural condition is assessed [9]. Fuzzy signatures can implement expert knowledge in a very plausible way, and using fuzzy signatures is very advantageous if not all the possible inputs are always present, and it can handle missing data in a very flexible way. This is the reason that our research focused on fuzzy signature-based methods [10; 11]. The flowchart for building our method can be seen in Figure 1, with the steps in the paper marked by green arrows. Figure 1: Flowchart of building our fuzzy signature-based decision support method. The green arrows indicate the target area of the present work. In our research, we selected the liver as the organ, and CT as the scanning method due to of the following reasons. One of the key organs during any kind of abdominal imaging is the liver, and incidental findings are very common. An incidental finding means any lesion that causes no symptoms and whose exploration was not the purpose of the examination. They include many benign lesions, but many malignant tumors and metastases are also found by screening \"healthy\" people or patients with subtle and general symptoms. Metastatic liver disease is many times more common than primary liver tumors. Finding liver metastasis means that the disease is advanced and possibly affects the whole body, so the cure needs to be changed to a more aggressive one. The reason why the liver is a common site for the remote spreading of tumors is its dual blood supply. Small lesions (!15 mm) of the liver are frequently detected by routine ultrasound (US) or computer tomography (CT) studies. About 30% of such lesions are malignant in the whole population; however, in the case of patients with previously known other malignancies, this ratio is 50% [12]. Targeted detection of liver metastases in patients with known neoplasms is primarily done by CT examination. These are so-called screening examinations, which can be followed by some other, more specific type, such as hepatocyte-specific MRI or even biopsies. A baseline CT is performed in almost every cancer case for reference. After treatment or after certain stage points, a control CT is performed to follow the recent changes. This is very important, but with the naked eye it is not always possible to detect subtle signs of metastases in CT and to differentiate them from benign lesions which can imitate malignancies when they are small. In the recent literature, the majority of the methods focused on the most frequent diseases in the liver, which is probably the reason for the low number of images of the less common lesions that could be used for training and testing. In the available studies, many of the methods can only distinguish malignant and benign lesions, without further differentiation [13; 14; 15; 16]. Many of the authors carried out research on a more detailed classification. There were experiments on differentiating between two or maybe three lesions that are significantly different in appearance [17; 18; 19]. However, there have been few researchers who could classify these diseases at the same time and not just distinguish between a few categories [20]. Many research groups studied only one phase of the CT protocol, either the non-enhanced [15; 20; 21; 22; 23] or the portal [24; 25; 26] phase, which is usually not sufficient for a proper decision, according to general medical opinion.