## 4 Discussion  
In order to analyze the effectiveness of the classifications using different numbers of texture features of spongy tissue images, the obtained results were compared with the results obtained by the author in previous studies. The results presented in [31,32] are based on the same set of images, but different approaches to processing textural features were used. Comparing the results allows for the assessment of the impact of the applied methods on the efficiency of classification, as well as for indicating the potential advantages or limitations of the analyzed method. In the study by [31], after the determination of the texture features of the tissue images, a selection process was performed. Data reduction consisted of feature selection, the aim of which was to reduce the initial set of 290 features to a smaller subset containing only those features that were most important from the point of view of the classification criterion. After data cleaning, which aimed to remove features with constant values, duplicates and strongly correlated, and the number of features was reduced to 18 for the reconstruction of soft tissues. The selection of features was performed using nine methods divided into three groups: filtering methods (Fisher method and analysis of variance and Relief method), wrapper methods (Sequential Forward Selection, Sequential Backward Selection, and Recursive Feature Elimination with the LogisticRegression estimator), and embedded methods (SelectFromModel with different estimators, such as Logistic Regression, AdaBoost, and LightGBM). Figure 9: Confusion matrices for (**a**) K-Nearest Neighbors and (**b**) Multilayer Perceptron classifiers. In the case of soft tissue reconstruction, the best models achieved a highest classification accuracy of 95% with a validation accuracy of 96%. Models that used different feature selection methods achieved identical TPR (sensitivity) and TNR (specificity) values of 95% each. The model based on NuSVM with an ANOVA feature selection method using 17 features achieved high accuracy and was one of the best. The model that used SVM with the RELIEF feature selection method and 18 features and the model based on NuSVM with the same RELIEF method also achieved very good results, achieving 95% in TPR and TNR. Models that used SVM and NuSVM with the RFE feature selection method and 18 features also achieved identical results (95% TPR and TNR). The aim of the research presented in the article by [32] was to determine the effect of normalizing CT spine images on the accuracy of automatic recognition of defects in the spongy tissue structure of the vertebrae in the thoracolumbar section. Feature descriptors were based on a gray level histogram, gradient matrix, RL matrix, event matrix, autoregressive model, and wavelet transformation. Six feature selection methods were used: Fisher coefficient, minimization of the probability of classification error with the accumulated correlation coefficient, mutual information, Spearman correlation, heuristic identification of confounding variables, and linear stepwise regression. The selection results were used to build six popular classifiers: Linear and Quadratic Discriminant Analysis, Naive Bayes Classifier, Decision Tree, K-Nearest Neighbors, and Random Forests. For the applied set of textural features and the selection and classification methods, image normalization significantly worsened the accuracy of the automatic recognition of osteoporosis based on CT spine images. The most accurate results obtained with quadratic discriminant analysis and K-nearest neighbors (K = 5) classification were ACC = 95%. Comparing the results presented in Table 7, it is possible to see differences in the performance of different classification models, both in terms of accuracy (ACC), sensitivity (TPR), and specificity (TNR). The best results were obtained in the two experiments of the current study, where KNN and MLP models with different numbers of features were used.