Multimodal MRI-based classification of migraine: using deep learning convolutional neural network

Hao Yang,Qihong Liu,Yi Wang,Junran Zhang

doi:10.1186/s12938-018-0587-0

Abstract

BackgroundRecently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number of migraineurs do not receive the accurate diagnosis when using traditional diagnostic criteria based on the guidelines of the International Headache Society. As such, there is substantial interest in developing automated methods to assist in the diagnosis of migraine.MethodsTo the best of our knowledge, no studies have evaluated the potential of deep learning technologies in assisting with the classification of migraine patients. Here, we used deep learning methods in combination with three functional measures (the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength) based on rs-fMRI data to distinguish not only between migraineurs and healthy controls, but also between the two subtypes of migraine. We employed 21 migraine patients without aura, 15 migraineurs with aura, and 28 healthy controls.ResultsCompared with the traditional support vector machine classifier, which has an accuracy of 83.67%, our Inception module-based convolutional neural network approach showed a significant improvement in classification output (over 86.18%). Our data also indicate that the Inception module-based CNN performs better than the AlexNet-based CNN (Inception module-based CNN reached an accuracy of 99.25%). Finally, we also found that regional functional correlation strength (RFCS) could be regarded as the optimum input out of the three indices (ALFF, ReHo, RFCS).ConclusionsOverall, our study shows that combining the three functional measures of rs-fMRI with deep learning classification is a powerful method to distinguish between migraineurs and healthy individuals. Our data also highlight that deep learning-based frameworks could be used to develop more complicated models or systems to aid in clinical decision making in the future.

Highlights

Deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification
Before development of the Inception module, the fundamental approaches used to improve accuracy of convolutional neural network (CNN) architecture were to increase the size of the layers and make the network deeper
The best performer out of three different features was the regional functional correlation strength (RFCS), and the highest identification rate achieved was 99.25% when using the Inception module-based CNN to distinguish between the healthy controls (HC) and migraine groups

Summary

Introduction

Deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. There is substantial interest in developing automated methods to assist in the diagnosis of migraine. A migraine is a common, chronic, incapacitating neurovascular disorder that is characterized by attacks of severe headaches, autonomic nervous system dysfunction, and in some patients, aura-associated neurologic symptoms [1]. As the diagnosis of migraines is based on a combination of features and it is relatively difficult to exclude possible causes, achieving an accurate diagnosis using traditional methods (e.g., symptoms analysis, medical tests) is not easy. There has been substantial interest in developing automated methods with the potential to assist in the diagnosis of migraines

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Discussion

Conclusion