Multiple sclerosis detection via 6-layer stochastic pooling convolutional neural network and multiple-way data augmentation
Background: 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.
Methods: In this article, we proposed a 6-layer stochastic pooling convolutional neural network (CNN)
with multiple-way data augmentation for multiple sclerosis detection in brain magnetic resonance imaging.
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. Further, we also conducted ablation experiments to
examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN
Results: 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.
Conclusion: 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
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