基于Radon变换的神经网络肺x线图像诊断

Chethan Sharma, S. Basheer, Anish Francis, V. Jayakrishna
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引用次数: 3

摘要

提出了一种新的基于人工学习的图像诊断方法,对射线图像肺结核患者进行神经网络学习预测诊断。肺部的x线图像通常难以诊断,因为它与肺癌相似。肺x线影像的过低诊断是一个难以解决的医学问题。本文将x射线图像的氡变换反馈到用Levenberg算法训练的反向传播神经网络中。目前的方法给出了清晰的结果,区分正常和异常图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radon transform processed neural network for lung X-ray image based diagnosis
A novel method for image diagnosis with artificial learning is presented-ray images tuberculosis patients is subjected to neural network learning for prediction of diagnosis. The X-ray images of lungs are normally difficult for diagnosis, since its similarity to lung cancer. Under and over diagnosis of lung X-ray images is a difficult medical problem to resolve. In the present work radon transform of the x-ray images is fed to back propagation neural network trained with Levenberg algorithm. The present methodology gives sharp results, distincting the normal and abnormal images.
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