Purpose

We used convolutional neural network (CNN) technology to improve the accuracy of diagnosis of knee meniscus injury and shorten the diagnosis time.

Method

We propose a meniscus detection method based on Fusion of CNN1 and CNN2 (CNNf), which uses Magnetic Resonance Imaging (MRI) and Computer tomography (CT) to compare the diagnosis results, verifies the proposed method through 2460 images collected from 205 patients in the hospital. We used accuracy, sensitivity, specificity, receiver operating characteristics (ROC), and damage total rate to evaluate performance.

Results

The accuracy of our model was 93.86%, the sensitivity was 91.35%, the specificity was 94.65%, and the area under the receiver operating characteristic curve was 96.78%. The total damage rate of MRI is 91.57%, which is far greater than the total damage rate of CT diagnosis of 80.13%.

Conclusion

CNNf-based MRI technology of knee meniscus injury has high practical value in clinical practice. It can effectively improve the accuracy of diagnosis and reduce the rate of misdiagnosis.

中文翻译：

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基于CNN1和CNN2融合的膝半月板损伤磁共振图像诊断与计算机断层扫描对比分析

目的

我们使用卷积神经网络（CNN）技术来提高膝半月板损伤诊断的准确性并缩短诊断时间。

方法

我们提出了一种基于 CNN1 和 CNN2 (CNNf) 融合的半月板检测方法，它使用磁共振成像 (MRI) 和计算机断层扫描 (CT) 来比较诊断结果，通过从 205 名患者中收集的 2460 张图像验证了所提出的方法。医院。我们使用准确性、灵敏度、特异性、接收器操作特性 (ROC) 和损坏总率来评估性能。

结果

我们的模型准确率为93.86%，敏感性为91.35%，特异性为94.65%，受试者工作特征曲线下面积为96.78%。MRI总损伤率为91.57%，远大于CT诊断80.13%的总损伤率。

结论

基于CNNf的膝关节半月板损伤MRI技术在临床上具有较高的实用价值。可有效提高诊断的准确性，降低误诊率。