Five-category classification of pathological brain images based on deep stacked sparse autoencoder

Abstract

Magnetic resonance imaging (MRI) is employed in medical treatment broadly, due to the quick development of computer technology. It is beneficial to classify the pathological brain images into healthy or other different categories automatically and accurately. This work aims to generate a pathological brain detecting system to classify the pathological brain images into five different categories of healthy; cerebrovascular disease; neoplastic disease; degenerative disease; and inflammatory disease. Our proposed method can be composed of the following several steps: First, we used data augmentation technology to deal with unbalanced distribution of the dataset. Then, we used deep stacked sparse autoencoder with minibatch scaled conjugate gradient to train the network, and the softmax layer is used as the classifier. As a result, the accuracy of our deep stacked sparse autoencoder over the test set is 98.6%. The prediction time of each image in test stage is only 0.070 s. Our experiment will be a powerful proof of the effectiveness of our proposed method that based on deep stacked sparse autoencoder.