Abstract：
UNASSIGNED: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages.
UNASSIGNED: Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs).
UNASSIGNED: The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control.
UNASSIGNED: The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model\'s highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984.
UNASSIGNED: The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.
UNASSIGNED: Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs).
UNASSIGNED: The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control.
UNASSIGNED: The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model\'s highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984.
UNASSIGNED: The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.
摘要：
背景：肺癌是最常见的癌症类型，占全球癌症病例的12.8%。当最初出现非特异性症状时，很难在早期诊断。
目的：使用机器学习方法开发的图像处理技术在决策支持系统的开发中发挥了至关重要的作用。本研究旨在通过深度学习方法和卷积神经网络（CNN）对良性和恶性肺部病变进行分类。
方法：图像数据集包括4459次计算机断层扫描（CT）扫描（良性，2242；恶性，2217).研究类型为回顾性；病例对照分析。一种基于GoogLeNet架构的方法,这是一种深度学习方法，用于对图像进行最大推断，并最大限度地减少手动控制。
结果：用于开发CNN模型的数据集包含在训练（3567）和测试（892）数据集中。模型在训练阶段的最高准确率估计为0.98。根据准确性,灵敏度,特异性，正预测值，和测试数据的阴性预测值，最高的分类性能比为阳性预测值,为0.984.
结论：深度学习方法有助于通过计算机断层扫描图像对肺癌进行诊断和分类。
目的：使用机器学习方法开发的图像处理技术在决策支持系统的开发中发挥了至关重要的作用。本研究旨在通过深度学习方法和卷积神经网络（CNN）对良性和恶性肺部病变进行分类。
方法：图像数据集包括4459次计算机断层扫描（CT）扫描（良性，2242；恶性，2217).研究类型为回顾性；病例对照分析。一种基于GoogLeNet架构的方法,这是一种深度学习方法，用于对图像进行最大推断，并最大限度地减少手动控制。
结果：用于开发CNN模型的数据集包含在训练（3567）和测试（892）数据集中。模型在训练阶段的最高准确率估计为0.98。根据准确性,灵敏度,特异性，正预测值，和测试数据的阴性预测值，最高的分类性能比为阳性预测值,为0.984.
结论：深度学习方法有助于通过计算机断层扫描图像对肺癌进行诊断和分类。
