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Background: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target for tumor targeted therapy drug selection. Therefore, the correct detection and evaluation of HER2 gene expression has important value for clinical treatment of breast cancer.Objective: The study goal is to better classify HER2 images.Methods: For general convolution neural network, with the increase of network layers, over fitting phenomenon is often very serious, which requires setting the value of random descent ratio, and parameter adjustment is often time-consuming and laborious, so this paper uses residual network, with the increase of network layer, the accuracy will not be reduced.Results: In this paper, a HER2 image classification algorithm based on improved residual network is proposed. Experimental results show that the proposed HER2 network has high accuracy in breast cancer assessment.Conclusion: Taking HER2 images in Stanford University database as experimental data, the accuracy of HER2 image automatic classification is improved through experiments. This method will help to reduce the detection intensity and improve the accuracy of HER2 image classification. [ABSTRACT FROM AUTHOR]

This paper proposes a new content recommendation system which combines the newly proposed embedded feature selection method and the new Fuzzy Temporal Logic based Decision Tree incorporated Convolutional Neural Network classifier. The newly proposed embedded feature selection called Fuzzy Decision Tree and Weighted Gini-Index based Feature Selection Algorithm (FDTWGI-FSA) that contains the existing incorporated the Fuzzy Decision Tree (FDT) and the Weighted Gini-index based Feature Selection Algorithm (WGIFSA) for getting optimized feature subset. Moreover, an enhanced CNN and Fuzzy Temporal Decision Tree for performing the deep learning process which is able to identify the exact e-content from the huge volume of data with the help of the recommended features by the proposed embedded feature selection method. The exact e-content can be identified after performing the five-layer network structure for extracting the relevant features and it also can be classified by applying the Fuzzy Temporal Decision Tree for the e-learners. Finally, the proposed content recommendation system provides exact content to the e-learners according to their level of understanding and it also satisfies them by providing the exact high level contents. The experiments have been conducted for evaluating the proposed content recommendation system and compared with the existing classifier including the standard CNN. [ABSTRACT FROM AUTHOR]