Brain CT Image Classification with Deep Neural Networks

With the development of X-ray, CT, MRI and other medical imaging techniques, doctors and researchers are provided with a large number of medical images for clinical diagnosis. It can largely improves 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 information for classification and pattern analysis. In the most recent literature, 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 artificial intelligence, have already been impacting a wide range of signal and information processing researches. By using the normal and abnormal 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 classification of brain health. It also shows that the stability of the network increases significantly as the depth of the network increasing.