This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well-organized classifiers, the convolutional neural network (CNN) and feature-based methodology, have been used. In the first step, the CNN classifier is optimized using a newly designed optimization method called the improved Harris hawk optimizer. This method is applied to the dataset, and the classification is commenced. If the disease cannot be detected via this method, the results are conveyed to the second classifier, that is, the feature-based method. This classifier, including Haralick and LBP features, is subsequently applied to the received dataset from the CNN classifier. Finally, if the feature-based method also does not detect cancer, the case study is healthy; otherwise, the case study is cancerous.

中文翻译：

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使用元启发式基于卷积神经网络和基于特征的分类器的新型计算机辅助肺癌检测

本研究提出了一种基于计算机断层扫描 (CT) 扫描图像的肺癌诊断系统，用于检测疾病。所提出的方法使用顺序方法来实现这一目标。因此，已经使用了两个组织良好的分类器，即卷积神经网络 (CNN) 和基于特征的方法。在第一步中，CNN 分类器使用新设计的优化方法进行优化，称为改进的 Harris hawk 优化器。将该方法应用于数据集，并开始分类。如果通过这种方法无法检测到疾病，则将结果传送到第二个分类器，即基于特征的方法。该分类器，包括 Haralick 和 LBP 特征，随后应用于从 CNN 分类器接收到的数据集。最后，如果基于特征的方法也没有检测到癌症，则案例研究是健康的；否则，案例研究就是癌症。