A Classifier to Detect Abnormality in CT Brain Images
Hassan Najadat, Yasser Jaffal, Omar Darwish, Niveen Yasser · 2011
Abstract — Medical images are among important data sources available, since these images are usually used by physicians to detect different diseases. Extracting features from brain CT images helps in building a machine classifier that able to classify new brain images without human interference. In this paper, we used a data set of 25 CT brain images with different diagnoses, and built a decision tree classifier that is able to predict general abnormality in human brain. The preprocessing uses the three stages described by Peng et al with modifications. The process of feature extraction was mainly to identify the regions of interest and extract analytical data from those regions. The model was evaluated using hold out method and N-fold evaluation. The results showed that the classifier is able to detect abnormality, even with a small training data set.