Introduction: Computer-aided diagnosis (CAD) systems have been developed with purpose to help doctors, primarily radiologists, in computer tomography (CT) image interpretation. In this diploma work, CAD systems for lung nodule classification and classification of liver pathologies are described. CAD systems were developed using machine learning. It usually contains four stages. The first stage is image preprocessing, to enhance image quality. The next stage is segmentation, to remove interesting objects from other image data. In the classification process, images with the same properties are combined into classes. It is a very complex process, for which we need to provide a huge amount of data to learn the relevant algorithms. We can provide it, using public or private databases. We need to seperate the information into training and testing data. We can calculate performance of systems using a confusion matrix. We can calculate sensitivity, specificity and accuracy. We can also measure performance using receiver operating characteristics curve (ROC) and value of area under the curve (AUC). Purpose: In this diploma work, systematic overview has been performed for CAD applications in CT lung and liver imaging. The main purpose was to review the architecture of CAD systems, its implementation in clinical practice and evaluate its performance Methods: In diploma work, we used a descriptive method and systematic analysis of numerous scientific articles from computer science and medicine. The five most relevant articles are included in the results. Results: Two CAD systems for lung nodule classification and three systems for the diagnosis of various liver pathologies are presented and systems have been tested on different databases. The description and evaluation of the system are tabulated. Discussion and conclusion: CAD systems are achieving great results. However, we have to bear in mind that there are some limitations. We conclude that the best results are achieved by systems that use deep learning algorithms. There are also problems in comparing results of CAD systems, because of the diversity of CT images and databases. A huge number of potentional application of CAD systems in clinical practice have not been reported yet, but we believe they will be applied in the future.
|
