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Development of cellular neural network algorithm for detecting lung cancer symptoms

机译:用于检测肺癌症状的细胞神经网络算法的开发

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Lung cancer is the most common of lethal types of cancer. One of the most important and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung nodules from x-ray image's result. Some of these lesions may not be detected because of camouflaged by the underlying anatomical structure, the low-quality of the images or the subjective and variable decision criteria used by doctors. Hence, a detection system using cellular neural network (CNN) is developed in order to help the doctors to recognize the doubtful lung cancer regions in x-ray films. In this study, a CNN algorithm for detecting the boundary and area of lung cancer in x-ray image has been proposed. Computer simulation result shows that our CNN algorithm is verified to detect some key lung cancer symptoms successfully and has been proved by radiologist.
机译:肺癌是最常见的致死性癌症。医生必须执行的最重要和最困难的任务之一就是根据X射线图像的结果来检测和诊断癌性肺结节。由于基本的解剖结构,图像质量低下或医生使用的主观和可变决策标准所掩盖的某些病变,可能无法检测到。因此,开发了一种使用细胞神经网络(CNN)的检测系统,以帮助医生识别X射线胶片中可疑的肺癌区域。在这项研究中,提出了一种用于在X射线图像中检测肺癌边界和面积的CNN算法。计算机仿真结果表明,我们的CNN算法已被验证可以成功检测出一些关键的肺癌症状,并已得到放射科医生的证明。

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