Name
Title | ||
|---|---|---|
Classification of Breast Masses in Mammogram Images Using Ripley's K Function and Support Vector Machine |
Abstract | ||
|---|---|---|
Female breast cancer is a major cause of death in western countries. Several computer techniques have been developed to aid radiologists to improve their performance in the detection and diagnosis of breast abnormalities. In Point Pattern Analysis, there is a statistic known as Ripley's Kfunction that is frequently applied to Spatial Analysis in Ecology, like mapping specimens of plants. This paper proposes a new way in applying Ripley's Kfunction to classify breast masses from mammogram images. The features of each nodule image are obtained through the calculate of that function. Then, the samples gotten are classified through a Support Vector Machine (SVM) as benign or malignant masses. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. The best result achieved was 94.94% of accuracy, 92.86% of sensitvity and 93.33% of specificity. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73499-4_59 | MLDM |
Keywords | Field | DocType |
support vector machine,svm,point pattern analysis,cause of death,machine learning,spatial analysis,breast cancer,structural risk minimization | Training set,K-function,Pattern recognition,Statistic,Computer science,Support vector machine,Point pattern analysis,Artificial intelligence,Structural risk minimization,Female breast cancer,Machine learning | Conference |
Volume | ISSN | Citations |
4571 | 0302-9743 | 10 |
PageRank | References | Authors |
0.64 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonardo de Oliveira Martins | 1 | 27 | 2.54 |
| Erick Corrêa da Silva | 2 | 10 | 0.64 |
| Aristofanes C. Silva | 3 | 316 | 36.48 |
| Anselmo C. Paiva | 4 | 379 | 48.88 |
| Marcelo Gattass | 5 | 382 | 48.43 |