The most effective and feasible method for treating cancer is early diagnosis of breast cancer. An appropriate software tool, known as computer-aided diagnosis, helps doctors identify or diagnose cancer in the early stages and more quickly, hence decreasing the mortality rate. Convolution neural networks (CNNs) have lately been utilized in medical image analysis, which can discover cancer cells or classify histopathology images by processing a large amounts of training data. In this study, we carried out transfer learning (TL) accompanied with a new self-learning algorithm for classification of breast cancer by processing the histopathological images. In this study, several pre-trained deep neural networks including, Inception V3 Net, VGG19, Alex net, ResNet-18, Google net, Shefflenet, Mobile net, Resnet 101, Inception ResnetV2 Net, and Squeeze net were examined. Using a self-learning technique for classification of sub-images instead of the whole image can increase the accuracy of cancer classification. In this technique, the main issue is that the true label of sub-images is unknown, and the classifier should be trained using noisy labels. Therefore, a hierarchal self-learning method is utilized to correct the false labels gradually. The main rule for correcting the false labels is designed based on some prior knowledge about the errors exist in the initial labels of sub-images. The proposed self-learning method reached the accuracy of 99.1%, after 4 level of label correction process accompanied with Inception-V3Net pretrained network. Overall, the use of self-learning DNNs for the classification of histopathological breast cancer images can improve the accuracy, efficiency, interpretability of the classification results and contribute to the development of personalized and precision medicine.