Abstract

The development of the computer-aided detection system placed an important role in the clinical analysis for making the decision about the human disease. Among the various disease examination processes, lung cancer needs more attention because it affects both men and women, which leads to increase the mortality rate. Traditional lung cancer prediction techniques failed to manage the accuracy because of low-quality image that affects the segmentation process. So, in this paper new optimized image processing and machine learning technique is introduced to predict the lung cancer. For recognizing lung cancer, non-small cell lung cancer CT scan dataset images are collected. The gathered images are examined by applying the multilevel brightness-preserving approach which effectively examines each pixel, eliminates the noise and also increase the quality of the lung image. From the noise-removed lung CT image, affected region is segmented by using improved deep neural network that segments region in terms of using layers of network and various features are extracted. Then the effective features are selected with the help of hybrid spiral optimization intelligent-generalized rough set approach, and those features are classified using ensemble classifier. The discussed method increases the lung cancer prediction rate which is examined using MATLAB-based results such as logarithmic loss, mean absolute error, precision, recall and F-score.

中文翻译：

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使用改进的深度神经网络和集成分类器从CT图像自动检测肺癌

摘要

计算机辅助检测系统的发展在做出有关人类疾病的决策的临床分析中发挥了重要作用。在各种疾病检查过程中，肺癌需要引起更多关注，因为它会影响男性和女性，从而导致死亡率上升。由于影响分割过程的图像质量低，传统的肺癌预测技术无法控制准确性。因此，本文介绍了一种新的优化图像处理和机器学习技术来预测肺癌。为了识别肺癌，收集了非小细胞肺癌CT扫描数据集图像。通过采用有效检查每个像素的多级亮度保留方法来检查收集的图像，消除了噪音并提高了肺部图像的质量。从去除噪声的肺部CT图像中，通过使用改进的深度神经网络对受影响的区域进行分割，该深度神经网络使用网络层对区域进行了分割，并提取了各种特征。然后借助混合螺旋优化智能广义粗糙集方法选择有效特征，并使用集成分类器对这些特征进行分类。讨论的方法提高了肺癌的预测率，使用基于MATLAB的结果（如对数损失，平均绝对误差，精度，召回率和 然后借助混合螺旋优化智能广义粗糙集方法选择有效特征，并使用集成分类器对这些特征进行分类。讨论的方法提高了肺癌的预测率，使用基于MATLAB的结果（例如对数损失，平均绝对误差，精度，召回率和 然后借助混合螺旋优化智能广义粗糙集方法选择有效特征，并使用集成分类器对这些特征进行分类。讨论的方法提高了肺癌的预测率，使用基于MATLAB的结果（例如对数损失，平均绝对误差，精度，召回率和F分。