Automatic detection of pulmonary embolism using computational intelligence.

Scurrell, Simon John
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Pulmonary embolism (PE) is a potentially fatal, yet potentially treatable condition. The problem of diagnosing PE with any degree of confidence arises from the nonspecific nature of the symptoms. In difficult cases, multiple tests will need to be performed on a patient before an accurate diagnosis can be made. These tests include Ventilation-Perfusion (V/Q) scanning, Spiral CT, leg ultrasound and d- Dimer testing. The aim of this research is to test the performance of using neural networks, namely Bayesian Neural Networks, to make a diagnosis based on available information. The information contains of a set of 12 V/Q scans which have been processed, and from which features have been extracted to provide inputs to the neural network. This system will act as a second opinion, and is not intended to replace an experienced clinician. The V/Q scans are analysed using image processing techniques in order to segment the lung from the background image and to determine if any abnormalities are present in the lung. The system must be able to discriminate between a genuine case of PE and of other diseases showing similar symptoms such as tuberculosis and parenchymal lung disease. Relevant features to be used in classification were then extracted from the images. The goal of this system is to make use of Bayesian neural networks. Using Bayesian networks, confidence levels can be calculated for each prediction the network makes. This makes them more informative than traditional multi layer perceptron (MLP) networks. The V/Q scans themselves are greyscale images of [256x256] size. In order to reduce redundancy and increase computational speed, Principal Component Analysis (PCA) is used to obtain the most significant information in each of the scans. Usually the Gold Standard for such a system would be pulmonary angiography, but in this case the Bayesian MLP (BMLP) is trained based on diagnosis by an experienced nuclear medicine physician. The system will be used to look at new cases for which the accuracy of the system can be established. Results showed good training performance, while validation performance was reasonable. Intermediate cases proved to be the most difficult to diagnose correctly.
Student Number : 0418382M - MSc(Eng) dissertation - School of Electrical Engineering and Information Technology - Faculty of Engineering and the Built Environment
neural networks, pulmonary embolism, image processing, Bayesian