Name
Title | ||
|---|---|---|
A Novel Breast Cancer Detection Technology Using an Advanced Transfer Maximal Entropy Clustering Algorithm |
Abstract | ||
|---|---|---|
The initial diagnosis of breast cancer involves analyzing the relevant examination report of the patient to determine whether the tumor is benign or malignant. Unsupervised clustering algorithms can be used with this type of problem. In a cluster analysis of a patient's examination data, the clustering results and the preliminary diagnosis results are obtained. However, due to the high cost of detection, medical datasets often have a small sample size or lack information. The traditional clustering technique usually has poor clustering effects in such scenarios. To solve this problem, this paper proposes an advanced transfer learning mechanism based on the classic maximum entropy clustering algorithm and proposes an advanced transfer maximal entropy clustering (AT-MEC) algorithm. A simulation experiment using the Wisconsin Breast Cancer Dataset is performed. This paper verifies that the proposed AT-MEC algorithm has a better clustering effect than other clustering algorithms in the Wisconsin Breast Cancer Dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1166/jmihi.2019.2775 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS |
Keywords | DocType | Volume |
Breast Cancer Diagnosis,Transfer Learning,Maximal Entropy Clustering (MEC),Wisconsin Breast Cancer Dataset | Journal | 9 |
Issue | ISSN | Citations |
8 | 2156-7018 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lifang Peng | 1 | 20 | 5.80 |
| Bin Huang | 2 | 1 | 1.04 |
| Kefu Chen | 3 | 1 | 1.04 |
| Leyuan Zhou | 4 | 7 | 3.13 |