Feature Extraction and Different Classifiers Applied for Detection of Abnormalities in Computer Tomography (CT) Images
Scientific Study from the year 2014 in the subject Medicine - Other, , language: English, abstract: Abnormality detection using classifiers is one of the recent research areas where much importance is given. It is one of the critical issues where excessive care needs to be taken for better diagnosis. An input image may contain excessive information either wanted or unwanted which depends upon the problem formulation. The problem in this project is to analyze the performance of the classifier in terms of its efficiency in detecting abnormalities in medical images. Any classifier needs to detect the carcinogenesis with respect to the efficiency in time of detection and performance. Here two classifiers are selected namely Singular Value Decomposition (SVD), and Principle Component Analysis (PCA). Both the SVD and PCA are applied for dual class classification procedure. The performance analysis of all these classifiers are analyzed using the classifier performance measures like, Sensitivity, Selectivity, Average Detection, Perfect Classification, Missed Classification, False Alarm, F-score and Quality Metrics. Here CT images of brain and skull are used for analysis. Two sets of 30 images are taken which contain both normal and abnormal ones.