Abstract
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.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China under Grant 61976063, the funding of Overseas 100 Talents Program of Guangxi Higher Education under Grant F-KA16035, the Diecai Project of Guangxi Normal University, 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program, research fund of Guangxi Key Lab of Multi-source Information Mining & Security (19-A-03-02), research fund of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, and the Young and Middle-aged Teachers’ Research Ability Improvement Project in Guangxi Universities under Grant 2020KY02030.
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Liang, X., Liu, J., Luo, Y., Wu, G., Zhang, S., Qiu, S. (2020). Automatic Segmentation and Diagnosis of Intervertebral Discs Based on Deep Neural Networks. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_19
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