Deep Convolutional Neural Networks for Automated Diagnosis of Disc Herniation on Axial MRI

In this study, deep convolutional neural networks (CNNs) are applied in computer-aided diagnosis of three types of disc herniation disease based on lumbar Axial MR Images. AlexNet architecture is explored and evaluated in order to classify images into four groups: Normal, Bulge, Protrusion and Extrusion. First, a large-scale dataset of 2329 scanned MRI images is gathered from local medical centers, then it is extended by using Visual Rotation and Mirror Reversal techniques to 9316 images. The CNNs are used for feature extraction and feature reduction in convolution layers; in addition, classification is performed by the CNN SoftMax layer. Also, to improve MR images resolution, CLAHE (Contrast-Limited Adaptive Histogram Equalization) and USM (UnSharp Masking) filters, which are of critical importance to the increase of the final accuracy, are employed. A region of interest (ROI) is then selected to reduce the size of the input images and eliminate additional features causing overhead on network. A number of changes are made to original AlexNet model including adding a Batch Normalization layer, as well as initial Xavier initialization, with remarkable effectiveness displayed in results. Also, instead of fully-connected layers, 1×1 convolution layer is applied to test accuracy. Finally, the proposed method is compared with the results of three state-of-the-art methods. Experimental results prove that this deep CNN results enjoy an improvement compared to former proposed methods; the accuracy, sensitivity and specificity for random sub sampling method are 87.75%, 86.5% and 94.75%, respectively, achieved by AlexNet architecture of CNN.