Name
Title | ||
|---|---|---|
Research on data classification and feature fusion method of cancer nuclei image based on deep learning |
Abstract | ||
|---|---|---|
There are many different types of nuclei in a tumor tissue. We can identify the specific nuclei and their distribution in the tissue to reflect the current cancer state of histopathological images. However, due to the existence of cellular heterogeneity, the recognition of nuclei in histopathological images has always been a problem of computer vision. In the paper, we use the transfer learning of Deep Convolutional Neural Network to classify nuclei, and found that adjusting the size of the nuclear image to a certain size can improve the accuracy of the nuclei classification model, while not significantly reducing the nuclei classification efficiency. Through further research, it was confirmed that the environment around the nuclei can bring great help to the model classification. Based on the principle, we design a feature fusion model. We extract features from nuclei image different sizes by CNN, fuse the features, and then use fully connected layer to classify the features. Experiments have proved that the feature fusion model has a considerable improvement in accuracy compared to the normal classification model. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1002/ima.22676 | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY |
Keywords | DocType | Volume |
cancer cell image, classification, deep learning, feature fusion model, multiscale image | Journal | 32 |
Issue | ISSN | Citations |
3 | 0899-9457 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shanshan Liu | 1 | 0 | 0.68 |
| Ruo Hu | 2 | 0 | 0.34 |
| Jianfang Wu | 3 | 0 | 0.34 |
| Xizheng Zhang | 4 | 0 | 0.68 |
| Jun He | 5 | 0 | 1.01 |
| Huimin Zhao | 6 | 1 | 1.03 |
| Huajia Wang | 7 | 0 | 1.35 |
| Xiangjun Li | 8 | 8 | 2.46 |