Using deep learning models for breast cancer detection of become or violent conditions

Breast cancer is a type of cancer that usually occurs in the lobule, duct and connective tissue regions of the breast and is caused by the abnormal movement of cells in these regions. The most common type of cancer in women is breast cancer. When the disease is detected early, cancerous cells can affect other organs through the blood and lymphatic vessels (metastasis state). Therefore, early diagnosis and treatment of breast cancer is important. This study proposes the classification of benign and malignant types of breast cancer with an artificial intelligence-based early diagnosis system. Blocky neural network models are now used in the proposed approach. Type-based features were extracted by adding a new fully connected layer to the last layer of ResNet models. In the next step, a new feature set was created by combining the features obtained from the fully connected layers. Softmax and machine learning methods were used while creating the classification with this feature set. With the approach proposed in the study, 100% overall accuracy was obtained from all the methods used in the classification creation steps. In this study, it was observed that obtaining and combining type-based fully connected layers positively affected the performance of experimental analyzes.