In this manuscript, a new approach using deep belief network (DBN) along opposition-based hybrid grasshopper and honey bee optimization algorithm for lung cancer classification is proposed. Chest computed tomography (CT) is commonly used to diagnosis the lung tumors. Initially, the image quality is improved by preprocessing techniques, and then the features like texture, color and shape are extracted. Several functions have been originating from the second order first gray level statistics like modified angles, high-order algebraic time invariant, Gaussian defining properties and new spectral power metrics. Recently, a local binary pattern geometric characteristic descriptor has been demonstrated in the extraction and classification of pulmonary nodules. Furthermore, some functionality is stripped away for comparison purposes. Dimension reduction is significant for the implementation of algorithms in machine learning. The other special approaches can be implemented to determine the functionalities due to vast set of features. To remove the unused and obsolete features between the feature selection approaches, the multivariate approach like local tangent space alignment is used. Finally, DBN is used to categorize pulmonary CT images as malignant or benign, and is calibrated to the detection of lung cancer classification by the opposition-based hybrid grasshopper and honey bee optimization algorithm. The Lung Image Database Consortium including Image Resources Initiative database are analyzed the network services, and the experimental results shows that the DBN network represents 97.52% accuracy, 96% sensitivity and 94.58% specificity.

中文翻译：

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用于肺癌分类的基于对立的混合蚱蜢和蜜蜂优化算法的最优深度置信网络：一种 DBNGHHB 方法

在这份手稿中，提出了一种使用深度置信网络 (DBN) 以及基于对立的混合蚱蜢和蜜蜂优化算法进行肺癌分类的新方法。胸部计算机断层扫描 (CT) 常用于诊断肺部肿瘤。首先通过预处理技术提高图像质量，然后提取纹理、颜色和形状等特征。一些函数源自二阶一阶灰度统计，如修正角度、高阶代数时不变、高斯定义属性和新的光谱功率度量。最近，在肺结节的提取和分类中已经证明了局部二进制模式几何特征描述符。此外，一些功能被剥离以进行比较。降维对于机器学习算法的实现具有重要意义。由于大量特征，可以实施其他特殊方法来确定功能。为了去除特征选择方法之间未使用和过时的特征，使用了多变量方法，如局部切线空间对齐。最后，DBN 用于将肺部 CT 图像分类为恶性或良性，并通过基于对立的混合蚱蜢和蜜蜂优化算法校准以检测肺癌分类。Lung Image Database Consortium 对包括 Image Resources Initiative 数据库在内的网络服务进行了分析，实验结果表明 DBN 网络具有 97.52% 的准确率、96% 的敏感性和 94.58% 的特异性。