Assisted Diagnosis of Lumbar Disc Herniation Based on Deep Learning
Aiming at the problem of difficulty in intelligent assisted diagnosis of complex lumbar spine bone structure,a deep learning based computer-aided diagnostic method framework is proposed to assist in the diagnosis of lumbar disc herniation(LDH).Firstly,a Resblock mod-ule is added to the encoding and decoding process of U-Net while preserving the skip connections of U-Net to enhance feature transfer in the target area,reduce feature loss,and accelerate model convergence speed.Secondly,the minimum envelope rectangle method is used to locate the center of the vertebrae,and ROI of appropriate size is cropped on the sagittal plane of the vertebrae based on the positioning,achieving ful-ly automatic ROI acquisition.Finally,in the Xception network,average pooling is used instead of Flatten operation,and BN layer,Droupout layer,and dynamic learning rate are added to improve the speed and accuracy of the model.Regarding the MRI case of lumbar disc herniation in a certain hospital in Shanghai,after evaluating and training the classification model evaluation criteria,it was found that the proposed framework had a diagnostic accuracy of 94.46%for ACC,94.60%for specific SPE,97.09%for sensitivity SEN,and 94.32%for accuracy PRE.Compared with previous studies,this has been improved,which is of great significance for promoting the clinical application of comput-er-aided diagnosis.
deep learningU-Netdiagnosis of lumbar disc herniationXceptionsegmentation of vertebraeintelligent aided diagnosis
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