Lung cancer is a leading cause of cancer related deaths in all around the world. The identification of lung nodules is the significant step in the diagnosis of earlier lung cancer which can develop into a tumor. In the lung disease analysis, valuable information is provided by the Computed Tomography (CT) scan. The key objective is to find the malignant lung nodules and categorize the lung cancer whether it is benign or malignant. In this paper, propose a diagnosis of lung cancer using hybrid deep neural network with adaptive optimization algorithm. Initially, the preprocessing stage is performed using the fast non local means (FNLM) filter. For the segmentation process, the Masi entropy based multilevel thresholding using salp swarm algorithm (MasiEMT-SSA) is used to segment the cancer nodule from the lung images. Using the grey-level run length matrix (GLRLM), different features are mined in the feature extraction. The binary grasshopper optimization algorithm (BGOA) is applied to select the optimum features for the feature selection (FS) process. Then the selected features are classified using the hybrid classifier named as deep neural network with adaptive sine cosine crow search(DNN-ASCCS) algorithm. The proposed hybrid classifier accurately detects the lung cancer. The proposed (DNN-ASCCS) is implemented by MATLAB using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) datasets. The different performance metrics are evaluated and related to the existing classifiers and different state-of-art approaches. The simulation outcomes verified that the developed scheme is achieved a high classification accuracy (99.17 %) compared to other approaches.

肺癌是世界各地与癌症相关的死亡的主要原因。肺结节的鉴定是诊断可能发展为肿瘤的早期肺癌的重要步骤。在肺部疾病分析中，计算机断层扫描（CT）扫描可提供有价值的信息。关键目标是发现恶性肺结节并对肺癌（无论是良性还是恶性）进行分类。本文提出了一种基于自适应优化算法的混合深度神经网络诊断肺癌的方法。最初，预处理阶段是使用快速非局部均值（FNLM）滤波器执行的。对于分割过程，使用Salp群算法（MasiEMT-SSA ）的基于Masi熵的多级阈值处理用于从肺部图像分割癌结节。使用灰度游程长度矩阵（GLRLM），可以在特征提取中挖掘不同的特征。应用二进制蚱hopper优化算法（BGOA）为特征选择（FS）过程选择最佳特征。然后，使用具有自适应正弦余弦乌鸦搜索（DNN-ASCCS）算法的称为深度神经网络的混合分类器对选定的特征进行分类。提出的混合分类器可以准确地检测出肺癌。所提出的（DNN-ASCCS）由MATLAB使用肺图像数据库联盟和图像数据库资源倡议（LIDC-IDRI）数据集实现。对不同的性能指标进行评估，并将其与现有的分类器和不同的最新方法相关联。