Abstract
Early detection of tumors is an important part of cancer treatment. In view of the existing algorithms: single data types, low feature extraction efficiency, and low classification network accuracy. A tumor types prediction model based on deep learning is proposed. The network model uses a Variational auto-encoder (VAE) to fuse the RNA expression and DNA methylation data of 32 tumor types, then uses the Hilbert curve to visualize it. Finally fuse data is sent to classification module: embed the attention module in the backbone network ResNet18 framework, convolutional layer instead of fully connected layer. The new sample is used to predict the tumor type. The experimental results show that this network model has excellent performance in tumor type classification and has important guiding significance for the early diagnosis of tumor patients.
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