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Lung Cancer Detection Using CT Image Based on 3D Convolutional Neural Network

机译:基于3D卷积神经网络的CT图像检测肺癌检测

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Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system, pattern recognition technique, backpropagation algorithm, etc. Recently, convolutional neural network (CNN) finds promising applications in many areas. In this research, we investigated 3D CNN to detect early lung cancer using LUNA 16 dataset. At first, we preprocessed raw image using thresholding technique. Then we used Vanilla 3D CNN classifier to determine whether the image is cancerous or non-cancerous. The experimental results show that the proposed method can achieve a detection accuracy of about 80% and it is a satisfactory performance compared to the existing technique.
机译:肺结核的早期发现对于肺癌的成功诊断和治疗具有重要意义。许多研究人员尝试了多样化的方法,例如阈值化,计算机辅助诊断系统,模式识别技术,背部识别算法等。最近,卷积神经网络(CNN)在许多领域找到了有前途的应用。在本研究中,我们研究了使用Luna 16数据集来检测早期肺癌的3D CNN。首先,我们使用阈值技术预处理原始图像。然后我们使用了Vanilla 3D CNN分类器来确定图像是癌性还是非癌症。实验结果表明,与现有技术相比,所提出的方法可以达到约80%的检测精度,并且是一种令人满意的性能。

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