Automatic Segmentation and Diagnosis of Intervertebral Discs Based on Deep Neural Networks

Lumbar disc diagnosis belongs to Magnetic Resonance Imaging (MRI) segmentation and detection. It is a challenge for even the most professional radiologists to manually check and interpret MRI. In addition, high-class imbalance is a typical problem in diverse medical image classification problems, which results in poor classification performance. Data imbalance is a typical problem in medical image classifications. Recently computer vision and deep learning are widely used in the automatic positioning and diagnosis of intervertebral discs to improve diagnostic efficiency. In this work, a two-stage disc automatic diagnosis network is proposed, which can improve the accuracy of training classifiers with imbalanced dataset. Experimental results show that the proposed method can achieve 93.08%, 95.41%, 96.22%, 89.34% for accuracy, precision, sensitivity and specificity, respectively. It can solve the problem of imbalanced dataset, and reduce misdiagnosis rate.