Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.
VISPNN: VGG-Inspired Stochastic Pooling Neural Network
