Title :
Machine Learning Techniques in Detecting of Pulmonary Embolisms
Author :
Myers, Mark H. ; Beliaev, Igor ; Lin, King-Ip
Author_Institution :
Univ. of Memphis, Memphis
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;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4370987
