Name
Abstract | ||
|---|---|---|
Breast cancer detection and classification using histological images play a critical role in the breast cancer diagnosis process. This paper presents a framework for autodetection and classification of breast cancer from microscopic histological images. The images are classified into benign or malignant. The proposed framework involves several steps which include image enhancement, image segmentation, features extraction, and images classification. The proposed framework utilizes a novel combination of K-means clustering and watershed algorithms in the segmentation step. We used K-means clustering to produce an initial segmented image and then we applied the watershed segmentation algorithm. Classification results show that the proposed method effectively detect and classify breast cancer from histological image with accuracy of 70.7% using a proposed Rule-Based classifier and 86.5% using a Decision Tree classifier. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/AICCSA.2018.8612799 | 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) |
Keywords | Field | DocType |
Digital Pathology,Microscopic Images,Image Segmentation,K-means,Watershed,Image Analysis,Breast Cancer,Benign,Malignant | Pattern recognition,Breast cancer,Computer science,Segmentation,Image segmentation,Watershed,Feature extraction,Real-time computing,Artificial intelligence,Cluster analysis,Classifier (linguistics),Decision tree learning | Conference |
ISSN | ISBN | Citations |
2161-5322 | 978-1-5386-9121-2 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qanita Bani Baker | 1 | 3 | 2.28 |
| Toqa' Abu Zaitoun | 2 | 0 | 0.34 |
| Sajda Banat | 3 | 0 | 0.34 |
| Eman Eaydat | 4 | 0 | 0.34 |
| Mohammad A. Alsmirat | 5 | 130 | 16.98 |