Name
Title | ||
|---|---|---|
Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification. |
Abstract | ||
|---|---|---|
Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10278-020-00372-8 | JOURNAL OF DIGITAL IMAGING |
Keywords | DocType | Volume |
Benign and malignant classification,Computer-aided diagnosis,Image enhancement,Multi-model ensemble architecture,3D CNN | Journal | 33.0 |
Issue | ISSN | Citations |
5.0 | 0897-1889 | 1 |
PageRank | References | Authors |
0.35 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Liu | 1 | 96 | 18.53 |
| Haichao Cao | 2 | 1 | 0.35 |
| Enmin Song | 3 | 176 | 24.53 |
| Guangzhi Ma | 4 | 24 | 5.32 |
| Xiangyang Xu | 5 | 76 | 10.40 |
| Renchao Jin | 6 | 30 | 8.83 |
| Chuhua Liu | 7 | 1 | 0.35 |
| Chih-Cheng Hung | 8 | 46 | 13.39 |