## 1 Introduction  
Breast cancer is currently the most common cancer globally and the second leading cause of cancer death in women today [1]. Supporting physician's diagnostic decisions has become an important application of artificial intelligence (AI), the role of which is expected to expand in the future [2]. Hybrid intelligent systems (HIS) in the field of medical pattern recognition have been widely applied for data classification. HIS combines intelligence tools and methods that make sense on a theoretical basis and can be applied practically. The unique characteristic of a HIS is that it can integrate two or more intelligent techniques that maintain their operating principles and attributes despite being fused or transformed. HIS performance depends on the selected intelligent systems and their architecture. In this paper, the HIS adaptive neuro-fuzzy inference system (ANFIS) is used to classify breast masses from digital mammograms that have also been processed with various enhancement and segmentation methods. A short review of related published work segmentation and classification follows below. There are many CADx systems that involve several processing steps and different methodologies for enhancement, segmentation, feature selection and classification. Previous works have accomplished great results, but in real testing data, there is always the question of whether they will be able to carry on. Therefore, the methods that are used on systems that detect pathologies and help the physician perform their work more efficiently have to become more advanced to overcome the demands that new images may present. This work focuses on the HIS ANFIS classification of digital mammographic masses that were previously enhanced by advanced fuzzy sets, as reported in our previous work [3]. K-means and Fuzzy C-means classifiers are also implemented for comparison purposes. The steps of the proposed classification algorithm are:  
* Advanced fuzzy sets for image enhancement. * Fused images based on OWA operators. * Global, Otsu and type-2 fuzzy sets for thresholding. * Feature extraction. * Feature selection. * Classification using K-means, Fuzzy C-means and ANFIS. Image enhancement contains fuzzy methods in order to enhance the image contrast. Five types of fuzzy sets have been used: linguistic hedges; a fuzzy enhancement function; and advanced fuzzy sets that are intuitionistic, Pythagorean and Fermatean fuzzy sets [4]. Two types of OWA operators have been used, with the target being the fusion of the fuzzy techniques and the creation of an image that would provide better results. Thresholding techniques are used so the enhanced image can undergo segmentation. Global and Otsu thresholding are classic methods for image segmentation [5]. Fuzzy sets type-2 is a thresholding method where a membership function is used, and the threshold index is extracted out of the ultrafuzziness of the image. The features that are extracted are based on the geometry of the thresholded image, the texture of the initial image and the fuzzy features from the enhanced fuzzified image. Classification is performed with the classic method of K-means and additionally more complicated methods of Fuzzy C-means (FCM) [6;7] and ANFIS technique that combines neural network and fuzzy inference engine methods. The novelty and contribution of this work rely on the combination of various elements for the optimum classification of mammographic masses. The proposed methods include:  
* An adaptive neuro-fuzzy inference system (ANFIS) for classification. * A hybrid classification procedure of mammographic masses based on FCM/K-means clustering algorithms using a decision tree. * Advanced fuzzy sets for contrast enhancement. In addition, the proposed methods are trained and tested on masses detected with FEDM, which is the current state-of-the-art in mammography. FEDM images differ significantly from digitized screen-film mammography (SFM) images and require either modified or new CADx approaches.