## 3 Results
### Classification of Samples Using Artificial Neural Networks  
Artificial neural networks were used to classify the samples based on their discriminants. Classification artificial neural network models of densely connected units were generated using the Statistica 13.3 package, using different color and texture discriminants as arguments. These models were characterized by the presence of a single hidden layer. Tables 1 and 2. present the results of learning artificial neural networks for four-class classification on the basis of GLCM discriminants calculated using functions in scikit-image packages or the MATLAB environment. Among the variables returned by the MATLAB environment functions, those least correlated with each other were selected (Figure 5). The top five models were selected based on the quality of testing and validation. The general observable correlation for both types of MLP models considered is low learning quality values, lower than test and validation quality values. This indicates the possibility of random occurrence of correctly classified cases and the uncertainty of the models. This may be related to the low set sizes and the random selection of elements into individual sets. The random selection of sets creates the risk that cases from a significantly different class are aggregated in a single set and that false high learning, testing, or validation quality values occur. The learning qualities of presented machine learning models are characterized by incomplete training against learning sets. A different behavior is characterized by deep artificial neural networks, for which 100% of the four-class classification quality was achieved in the case of a network analyzing images expressed on an 8-bit greyscale (Figure 6a). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Name of ANN** & **MLP 4-3-4** & **MLP 4-10-4** & **MLP 4-3-4** & **MLP 4-6-4** & **MLP 4-7-4** \\ \hline Quality of learning & 40.476 & 43.651 & 34.127 & 27.778 & 43.651 \\ Quality of testing & 48.148 & 55.556 & 51.852 & 51.852 & 55.556 \\ Quality of validation & 55.556 & 51.852 & 51.852 & 51.852 & 51.852 \\ Algorithm of training & BFGS 21 & BFGS 52 & BFGS 35 & BFGS 9 & BFGS 28 \\ Error function & Entropy & SOS & Entropy & Entropy & SOS \\ Hidden activation & Exponential & Exponential & Tanh & Tanh & Logistic \\ Output activation & Softmax & Linear & Softmax & Softmax & Tanh \\ \hline \end{tabular}
\end{table} 
Table 1: Results of learning artificial neural networks created in Statistica 13.3, together with the parameters of the best models created for the four-class classification on the basis of the GLCM characteristics calculated using MATLAB (autocorr, homom, cshad, inf1h, corrn, cpron). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Name of ANN** & **MLP 6-11-4** & **MLP 6-11-4** & **MLP 6-10-4** & **MLP 6-10-4** & **MLP 6-10-4** & **MLP 6-8-4** \\ \hline Quality of learning & 66.67 & 32.54 & 34.92 & 37.3 & 36.51 \\ Quality of testing & 55.56 & 40.74 & 40.74 & 44.44 & 40.74 \\ Quality of validation & 44.44 & 48.15 & 51.85 & 48.15 & 48.15 \\ Algorithm of training & BFGS 60 & BFGS 7 & BFGS 4 & BFGS 9 & BFGS 6 \\ Error function & Entropy & SOS & Entropy & SOS & Entropy \\ Hidden activation & Tanh & Logistic & Linear & Logistic & Linear \\ Output activation & Softmax & Logistic & Softmax & Exponential & Softmax \\ \hline \end{tabular}
\end{table} 
Table 2: Results of learning artificial neural networks created in Statistica 13.3, together with the parameters of the best models created for the four-class classification on the basis of the GLCM characteristics calculated using MATLAB (autocorr, homom, cshad, inf1h, corrn, cpron). 17% of misclassified cases were assessed by the network as class 3. In the test set, 78% of cases belonging to class 3 were classified correctly, while confusions were assessed as belonging to classes 1 and 2 (Figure 7a). Moreover, 72% of cases belonging to class 2 were classified correctly, while 22% were confused with class 3.