Neurologist-level classification of stroke using a Structural Co-Occurrence Matrix based on the frequency domain

This paper presents a new approach to automatically classify hemorrhagic and ischemic strokes using the Structural Co-Occurrence Matrix (SCM) extracted from the main frequencies of computed tomography images of the brain. The main advantage of this approach is that it only uses the image as a parameter. Specificity, sensitivity, positive predictive value, F-Score, harmonic mean, and accuracy were used as metrics to evaluate the efficiency of the SCM. The SCM was compared with local binary patterns, gray-level co-occurrence matrices, invariant moments of Hu and with the feature extraction method based on human tissue density patterns named Analysis of Human Tissue Densities. Multiple machine learning classifiers were used including support vector machine, multilayer perceptron, minimal learning machine and the linear discriminant analysis. The results show that the SCM in the frequency domain can extract the most discriminant structural information of strokes automatically, obtaining good results without the needed of additional parameters.