利用卷积神经网络对杜兴氏肌营养不良症进行分类识别

Classification and Identification of Magnetic Resonance Images of Duchenne Muscular Dystrophy with Convolutional Neural Network

杜兴氏肌营养不良(DMD)是一种致死性骨骼肌遗传病。传统的诊断方法中包含创伤性检查,会令患者产生极大的痛苦。本文基于受试者的磁共振图像(MRI),探索DMD的无创检测方法。共获取试验图像485幅,分成患者组和健康对照组,每组均包括T1和T2这2类加权图像。试验设计一个10隐层卷积神经网络(CNN),利用该网络直接读取T1和T2并分类识别。结果显示:1)随着网络参数的优化和迭代次数的增加,图像识别准确度分别达到99. 2%和98. 9%,结果收敛且稳定; 2) 2类加权图像T1和T2均能很好区分患者组与健康组; 3)与KNN、LR、DT及SVM等算法相比,CNN算法的分类准确度更高。CNN尤其提升了对于T2图像的识别准确度,大大发掘了T2图像的利用价值。因此,利用CNN对DMD图像进行分类识别,因其准确度高、无损图像信息等特点,有望为临床提供客观有效的辅助诊断手段。这是人工智能在DMD无创检测领域中有效的尝试探索。

Duchenne muscular dystrophy (DMD) is a fatal skeletal muscle hereditary disease. The conventional treatment is invasive, which incurs great sufferings. Therefore, this paper explores a non-invasive detection method on the basis of magnetic resonance images (MRI) of the patients. 485 experimental MRIs are obtained with the guidance of senior physicians of neuromuscular department. These images are divided into two control groups: the patient group and the healthy group;each group includes two weighted images, T1 and T2. A 10-hidden layer depth convolutional neural network (CNN) is designed and used to directly read T1 and T2, and classify them. The results show: firstly, by increasing the numbers of network parameters and iterative optimizations, the accuracies of image recognition have reached 99.2% and 98.9% respectively;secondly, both T1 and T2 can be used to well distinguish between patient and healthy groups;thirdly, in comparison with KNN, LR, DT and SVM algorithms, the accuracy of classification with the CNN algorithm is best. In particular, the CNN algorithm improves the recognition accuracy of T2 images, and greatly explores the utilization value of T2 images. Therefore, using CNN for DMD image classification and recognition, because of its high accuracy, lossless image information and other characteristics, it is expected to provide an objective and effective auxiliary diagnosis means for clinical;this is a new exploration of application of artificial intelligence in the field of DMD non-invasive detection.