Intelligent medical assistant diagnosis method based on data fusion
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摘要: 针对医生诊断需要结合临床症状、影像检查等各种数据,本文提出了一种可以数据融合的医疗辅助诊断方法。该方法将患者的影像信息(如CT图像)和数值数据(如临床诊断信息)相结合,利用结合的信息自动预测患者的病情。基于该方法,提出了基于深度学习的医疗辅助诊断模型,模型以卷积神经网络为基础进行搭建,图像和数值数据作为输入,输出病人的患病情况。因此该医疗辅助诊断方法能够利用更加全面的信息,有助于提高自动诊断准确率和降低诊断误差;另外,仅使用提出的医疗辅助诊断模型就可以一次性处理多种类型的数据,能够在一定程度上节省诊断时间。在两个数据集上验证了提出的方法的有效性,实验结果表明,该方法是有效的,它可以提高辅助诊断的准确性。Abstract: This paper presents a new method of medical assistant diagnosis for medical field. This method combines the image information of patients (such as CT image) and numerical data (such as clinical diagnosis information), and uses the combined information to automatically predict the patient's condition. Based on this method, a medical assistant diagnosis model based on deep learning is proposed. The model takes image and numerical data as input, and outputs the patient's condition. Therefore, this method can use more comprehensive information, help to improve the accuracy of automatic diagnosis and reduce the diagnosis error; in addition, using the proposed model can process multiple types of data at one time, which can save the diagnosis time to a certain extent. The effectiveness of the proposed method is verified on two datasets. The experimental results show that the method is effective and can improve the accuracy of auxiliary diagnosis.
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Key words:
- Image classification /
- Convolution neural network /
- Feature fusion /
- Medical diagnosis /
- Deep learning
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