MwoA auxiliary diagnosis using 3D convolutional neural network

Abstract

Migraine is a brain disease that seriously endangers human health in which migraine without aura accounts for the largest proportion in the clinic and is challenging to diagnose. Currently, the auxiliary diagnosis methods based on functional connectivity analysis combined with machine learning algorithms is an important research domain for migraine without aura. Although a few earlier studies have made significant progress, it is still hard to meet the clinical and research needs. The main reason is that the functional connectivity analysis methods mostly rely on the prior template, which is easily affected by subjective factors and the performance of the classifier, the intelligence and accuracy are still at a low level. In this paper, we propose an intelligent auxiliary diagnosis algorithm for migraine without aura based on improved 3D convolutional neural network dubbed MwoA3D-Net. To avoid the difference results caused by varying prior templates, a group information guided independent component analysis method is employed to obtain the resting state network for training the MwoA3D-Net algorithm. Subsequently, the MwoA3D-Net algorithm is applied to diagnose migraine without aura patients and healthy controls automatically. Several optimization strategies, such as 3D data augmentation and L2 regularization, are introduced to prevent overfitting effectively. Experimental results on a data set of 65 migraine without aura patients and 60 healthy subjects show that MwoA3D-Net has a highly robust performance, with an average diagnostic accuracy of 98.40%. Furthermore, the selected resting-state brain function network has robust identification and can be adopted as potential biomarkers of migraine without aura toward individualized diagnosis.