Abstract: 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.

Key words:  DMD, magnetic resonance image, computer-aided detection, artificial intelligence, convolutional neural network