Brain CT Image Classification with Deep Neural Networks

Abstract

With the development of X-ray, CT, MRI and other med-ical imaging techniques, doctors and researchers are provided with a large number of medical images for clinical diagnosis. It can largely im-proves the accuracy and reliability of disease diagnosis. In this paper, the method of brain CT image classification with Deep neural networks is proposed. Deep neural network exploits many layers of non-linear in-formation for classification and pattern analysis. In the most recent lit-erature, deep learning is defined as a kind of representation learning, which involves a hierarchy architecture where higher-level concepts are constructed from lower-level ones. The techniques developed from deep learning, enriched the main research aspects of machine learning and ar-tificial intelligence, have already been impacting a wide range of signal and information processing researches. By using the normal and abnor-mal brain CT images, texture features are extracted as the characteristic value of each image. Then, deep neural network is used to realize the CT image classification of brain health. Experimental results indicate that the deep neural network have performed well in the CT images classi-fication of brain health. It also shows that the stability of the network increases significantly as the depth of the network increasing.