Name
Abstract | ||
|---|---|---|
This paper examines the potential contribution of infrared (IR) imaging in breast diseases detection. It compares obtained results using some algorithms for detection of malignant breast conditions such as Support Vector Machine (SVM) regarding the consistency of different approaches when applied to public data. Moreover, in order to avail the actual IR imaging's capability as a complement on clinical trials and to promote researches using high-resolution IR imaging we deemed the use of a public database revised by confidently trained breast physicians as essential. Only the static acquisition protocol is regarded in our work. We used 102 IR single breast images from the Pro Engenharia (PROENG) public database (54 normal and 48 with some finding). These images were collected from Universidade Federal de Pernambuco (UFPE) University?s Hospital. We employed the same features proposed by the authors of the work that presented the best results and achieved an accuracy of 61.7 % and Youden index of 0.24 using the Sequential Minimal Optimization (SMO) classifier. |
Year | Venue | Keywords |
|---|---|---|
2014 | Systems, Signals and Image Processing | biological tissues,diseases,image classification,image resolution,infrared imaging,medical image processing,object detection,support vector machines,ir single breast images,proeng public database,pro engenharia,smo classifier,svm,ufpe university hospital,universidade federal de pernambuco university,youden index,breast disease detection,breast disease diagnosis,breast physicians,clinical trials,high-resolution ir imaging,malignant breast condition detection,sequential minimal optimization,static acquisition protocol,support vector machine,thermographic image,breast cancer,computer-aided diagnosis (cad),pattern recognition,thermography,biomedical imaging,accuracy,indexes |
Field | DocType | ISSN |
Acquisition Protocol,Pattern recognition,Computer science,Medical imaging,Support vector machine,Youden's J statistic,Artificial intelligence,Sequential minimal optimization,Classifier (linguistics) | Conference | 2157-8672 |
Citations | PageRank | References |
6 | 0.59 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| e o rodrigues | 1 | 27 | 4.50 |
| A. Conci | 2 | 49 | 4.40 |
| Tiago B. Borchartt | 3 | 40 | 3.36 |
| andre campos paiva | 4 | 6 | 0.59 |
| a correa silva | 5 | 6 | 0.59 |
| trueman machenry | 6 | 10 | 1.12 |