The number of mitotic tumor cells detected in each slide is one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counts are still a difficult problem for pathologists and related experts. Traditional methods use manual design algorithms to extract features of mitotic cells, and most methods rely on sliding windows to achieve pixel-level classification through deep learning. However, the complex background and high resolution of pathological images make the above methods time-consuming and ineffective. In order to solve the above problems, we propose a new cascaded convolutional neural network UBCNN (cascaded CNN based on UNet), which consists of three parts: semantic segmentation and classification to detect mitosis. First, we use an improved UNet ++ segmentation network to locate the candidate set of mitotic targets. Secondly, an adequately labeled cell nucleus data set is sent to an improved two-dimensional VNet network, and the cell nucleus is located by means of semantic segmentation to obtain accurate image blocks of mitotic and non-mitotic cells. Finally, the obtained cell image block is used to train a convolutional neural network to achieve binary classification, and the candidate set area is screened to retain the final result of mitosis cells. This paper verifies the detection effect of the above-mentioned cascade detection algorithm on the ICPR 2012 and 2014 mitosis automatic detection competition data sets. The evaluation indicators include accuracy, recall and F-score. Our cascade detection algorithm based on segmentation and classification reached 0.831 on the ICPR 2012 data set and 0.576 on the ICPR 2014 data set. Compared with other existing algorithms, the detection effect was improved, which was very competitive.

This paper applies thermal imaging technology to gearbox fault diagnosis. The temperature field calculation model is established to obtain the temperature field images of various faults. A deep learning network model combining transfer learning of convolutional neural network with supervised training and unsupervised training of deep belief network is proposed. The model requires one-fifth of the training time of the convolutional neural network model. The data set used for training the deep learning network model is expanded by using the temperature field simulation image of the gearbox. The results show that the network model has over 97% accuracy for the diagnosis of simulation faults. The finite element model of gearbox can be modified with experimental data to obtain more accurate thermal images, and this method can be better used in practice.