Comparative Analysis of Deep Neural Network and Texture Based Classifiers for Recognition of Acute Stroke using Non Contrast CT Images
This work presents a computer technology for automatic recognition of acute stroke using non contrast computed tomography brain images. The early diagnosis of acute stroke is of primary importance for deciding on a method for further treatment, and the developed system aims at assisting a radiology specialist in the decision making process. We consider deep neural network and texture based classifiers in order to compare their efficiency on a moderate sized sample of patients with acute stroke. We use U net as a basic architecture of the neural network, and Haralick textural features, extracted from images, for kNN, SVM, Random Forest and Adaboost classifiers. Experiments with real CT images using cross validation technique show that deep neural network outperforms the considered texture based classifiers; however, the latter are faster in training. We demonstrate that texture based approach is able to give potentially useful additional information for stroke recognition, such as estimates of textural features importance; visualization of differences in positive and negative class distributions.
2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)