11 March 2011 A neural network learned information measures for heart motion abnormality detection
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79621H (2011) https://doi.org/10.1117/12.878028
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
In this study, we propose an information theoretic neural network for normal/abnormal left ventricular motion classification which outperforms significantly other recent methods in the literature. The proposed framework consists of a supervised 3-layer artificial neural network (ANN) which uses hyperbolic tangent sigmoid and linear transfer functions for hidden and output layers, respectively. The ANN is fed by information theoretic measures of left ventricular wall motion such as Shannon's differential entropy (SDE), Rényi entropy and Fisher information, which measure global information of subjects distribution. Using 395×20 segmented LV cavities of short-axis magnetic resonance images (MRI) acquired from 48 subjects, the experimental results show that the proposed method outperforms Support Vector Machine (SVM) and thresholding based information theoretic classifiers. It yields a specificity equal to 90%, a sensitivity of 91%, and a remarkable Area Under Curve (AUC) for Receiver Operating Characteristic (ROC), equal to 93.2%.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. S. Nambakhsh, Kumaradevan Punithakumar, Ismail Ben Ayed, Aashish Goela, Ali Islam, Terry Peters, and Shuo Li "A neural network learned information measures for heart motion abnormality detection", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621H (11 March 2011); doi: 10.1117/12.878028; https://doi.org/10.1117/12.878028
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