## 1 Introduction
### Related Works  
In papers [8; 9; 10], the authors analyze, evaluate and review existing artificial intelligence-based classification and segmentation techniques proposed in the last years for early detection and diagnosis of breast cancer using medical images. [PERSON] et al. [11] created a deep neuro-fuzzy network based on definitions of Takagi-Sugeno-Kang and tested the classifier on three datasets. The highest accuracy that they achieved was 99.58%. [PERSON] et al. [12] represented a review of deep and machine learning techniques for mammography, assuming that the most frequent supervised methods for classification are artificial neural networks (ANNs), support vector machine (SVM) and random forest (RF). The most frequent unsupervised methods are the clustering algorithms of K-means, principal component analysis (PCA) and singular value decomposition (SVD). In deep learning, convolutional neural networks (CNNs) are one of the most used algorithms. Currently, computers that try to simulate the human brain are far from the learning capabilities of it. [PERSON] et al. [13] proposed the deep reinforcement learning-based Deep Q learning (DQL) method as a classifier and the gorilla troops optimization (GTO) algorithm for feature selection. The method GTO-DQL was tested on three datasets with an accuracy up to 99.02%. [PERSON] et al. [14] proposed an algorithm named breast mass classification system (BMC) that is based on K-means, long short-term memory network of recurrent neural network (RNN), CNN and random forest. The proposed BMC system was tested on DDSM and MIAS dataset. For DDSM, the accuracy and AUC were 96% and 94-97%, respectively. For MIAS, the accuracy and AUC were 95% and 94-98%, respectively. [PERSON] et al., in [15], introduced the Dual Core Net (DCN), which computes mass segmentation and classifies simultaneously. The proposed algorithm was tested on DDSM and INbreast datasets, achieving 92.27% DI coefficient and 85% AUC for DDSM and 93.69% DI coefficient and 93% AUC for INbreast dataset. [PERSON] et al., in [16], proposed a procedure for breast cancer masses that includes the Watershed segmentation and a hybrid machine learning classifier that is based on multi-layer perceptron (MLP), J48 and K-means. The methods were tested on the database from the UCI Machine Learning Repository and achieved an accuracy of over 88%. A paper by [PERSON] et al. focused on the decision tree classification algorithm [17]. In [18], a novel mass detection process that includes enhancement, characterization and classification was described. The classification mechanism is based on Bayesian regularization back-propagation networks and ANFIS techniques. [PERSON] et al. [19] present a review of the application of the adaptive neuro-fuzzy inference system as a classifier in medical image classification during the past 16 years. The most common deep networks that are used for image processing are convolutional neural networks, recurrent networks, and generative adversarial models [20]. The authors [21] proposed a novel method of an ensemble classifier set and depthwise separable convolution to extract shallow features. [PERSON] et al. [22] represented and developed a convolutional neural network model for brain tumor segmentation. [PERSON] et al. [23] introduced a modified version of InceptionV3 and ResNet50 classifiers that are similar to CNN and tested the procedure on the DDSM dataset. They managed to obtain an 85.7% accuracy. [PERSON] et al. [24] tested segmentation and classification methods in order to identify masses as malignant or benign. Fully, CNNs were introduced. The technique where applying data augmentation with a modified U-Net model and classifying the data with the InceptionV3 method achieved 98.87% accuracy and 98.88% AUC. [PERSON] et al. [25] represented two deep learning-based ensemble models. They used the VGG16, InceptionV3 and VGG19 as base classifiers and the two ensemble models were trained.