Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.

乳腺病变的早期诊断以及恶性病变与良性病变的区别对于乳腺癌的预后非常重要。在这种疾病的诊断中，超声是一种非常重要的放射成像方法，因为它可以进行活检和病变特征鉴定。由于超声诊断取决于专家，因此用户的知识水平和经验非常重要。另外，计算机辅助系统的贡献非常高，因为当与医院病患密集的患者一起考虑时，这些系统可以减少放射科医生的工作量并增强他们的知识和经验。在本文中，开发了一种基于混合的CNN系统，用于诊断乳腺癌的良性，恶性和正常病变。Alexnet，MobilenetV2，和Resnet50模型用作混合结构的基础。这些使用的模型的功能分别获得和连接。因此，增加了使用的特征的数量。后来，通过mRMR（最小冗余最大相关性）特征选择方法选择了这些特征中最有价值的特征，并使用诸如SVM，KNN的机器学习分类器对它们进行了分类。在SVM分类器中以95.6％的准确率获得最高比率。