Classification of Posterior Fossa CT Brain Slices using Artificial Neural Network

Abstract

The diagnosis of ischemic stroke in Posterior Fossa (PF) using conventional Computed Tomography (CT) is limited. Meanwhile, the identification of PF slices in CT is a very complicated task due to their varying structures for each slices. Traditionally, the identification of PF slices by the radiologist is based on the PF structure that present in the CT images. This procedure can introduce misidentification issue and time consuming. In this paper, a method for automated classification of PF slices in CT brain images is investigated to sort the slice that contains PF for further study of ischemic stroke detection. This framework employs 11 features of Gray Level Co-Occurrence Matrix (GLCM). All the obtained features have been passed through Artificial Neural Network (ANN) classifier using Multilayer Perceptron (MLP) architecture. The experimental results show that, the combination of GLCM with ANN is promising to be applied in the classification of PF slices as they can provide 93%, 92.5% and 94.2% of precision, recall, and accuracy average rate respectively with average processing time of 0.36 seconds per slices.