## 3 Results  
To extract the results of the methods, we used computer resources with the following characteristics: Dell PE 350, CPU: Intel Xeon E-2314 2.8 GHz, 8 M Cache, DELL Memory 16 GB--DDR4 UDIMM 3200 MHz, Windows server 2019 standard edition 16 Cores 2 VMs. At the ROIs of the mammograms, median filtering and the methods that were proposed in our previous work were performed. The methods provided seven enhanced images, LH-CON, LH-XWW, IFS, PFS, FFS, OWA-GA and OWA-HK, with one initial ROI mammography as an origin [3]. Furthermore, in this paper, we performed thresholding, feature extraction, feature selection and classification of the mammograms. Each enhanced image is thresholded by three different methods of thresholding. Global, Otsu and type-2 fuzzy sets thresholding provide a total of twenty-one thresholded images. The images that we created with the initial images will provide the features that we need for the classifier. Geometrical, texture and fuzzy features are extracted according to the feature extraction section of this paper. This procedure happens to all the mammograms of the dataset. At the end of this step, there will be three three-dimension matrixes. The first matrix has rows of the ninety-seven mammograms; c columns of the nine features that are extracted from each mammography; and, as the depth of the matrix, the seven enhancement methods. The first three-dimensional matrix is about the features that are extracted with the method of global thresholding, the second three-dimensional matrix is about the method of Otsu thresholding and the third three-dimensional matrix is about the method of type-2 fuzzy sets thresholding. The data of the three matrixed are normalized at this stage. The normalization is performed on each feature of the mammograms. The feature selection is the exhaustive search where all the  
Figure 10: Extraction of extracted ROI [3]. combinations of the features are tested with the K-means, FCM and ANFIS classifiers so the best combination can be found. K-means, FCM and ANFIS classifiers have two up to nine inputs that depend on the combination that the exhaustive search gives. For the ANFIS classifier, we used two membership functions at the input, type \"gbell\", and the rules are constant. The dataset was divided into training and testing sets. The training set was 70% of the data, and the testing set was 30%. The classifiers were trained first with the training set, and the performance indicators were recorded from the testing set. The sets were randomly selected. The performance of the classifiers was tested based on the accuracy. The classification procedure was performed ten times so it can be statistically more accurate, and the results for each classifier are the average of the ten results. The following Tables 1-3 show the results. Table 1 shows the results of the global thresholding method, Table 2 shows the results of Otsu thresholding and Table 3 shows the type-2 fuzzy sets thresholding method. For each line, the first subline is the results of K-means clustering, the second subline of FCM and the third subline of ANFIS. Based on Table 1, the enhancement method FFS with global thresholding and the FCM classifier scored the highest indicator values with an accuracy of 95.17%, F1 score of 86.98% and AUC of 0.91 (ROC curve in Figure 10(a)). Based on Table 2, the enhancement method LH-XWW with Otsu thresholding and FCM classifier scored the highest indicator values with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91 (ROC curve in Figure 10(b)). Based on Table 3, the enhancement method LH-XWW with type-2 fuzzy sets thresholding and FCM classifier scored the highest indicator values with an accuracy of 94.14% and an AUC of 0.89 (ROC curve in Figure 10(c)). The best F1 score for type-2 fuzzy sets is achieved with the enhancement method OWA-HK and the classifier FCM, with a score of 86.22% (ROC curve in Figure 10(d)). In general, the best scores were achieved by the FCM classifier.