Machine Learning Techniques in Detecting of Pulmonary Embolisms

Author

Myers, Mark H. ; Beliaev, Igor ; Lin, King-Ip

Author_Institution

Univ. of Memphis, Memphis

fYear

2007

fDate

12-17 Aug. 2007

Firstpage

385

Lastpage

390

Abstract

Computer Aided Detection (CAD) systems have recently been used by physicians to help automatically detect early forms of breast cancer in X-ray images, lung nodules in lung CT images, and polyps in colon CT images. We discuss an automatic detection mechanism using a genetic algorithms (GA) approach to identify and classify Pulmonary Embolisms (PE) captured through Computed Tomography Angiography (CTA). Our method enhances the performance of the classification of diseases as compared to other methodologies discussed in this paper.

Keywords

computerised tomography; diagnostic radiography; diseases; genetic algorithms; image classification; learning (artificial intelligence); mammography; medical image processing; multilayer perceptrons; X-ray imaging; breast cancer; computed tomography angiography; computer aided pulmonary embolism detection; disease classification; genetic algorithm; k-nearest neighbour; lung CT image; machine learning technique; multilayer perceptron; Breast cancer; Cancer detection; Colonic polyps; Computed tomography; Lungs; Machine learning; Physics computing; X-ray detection; X-ray detectors; X-ray imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Conference_Location

Orlando, FL

ISSN

1098-7576

Print_ISBN

978-1-4244-1379-9

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2007.4370987

Filename

4370987