肺癌是所有癌症相关疾病中最常见的死亡原因。患者的肺部扫描检查是主要的诊断技术。该扫描分析涉及 MRI、CT 或 X 射线。由于对患者肺部进行成像涉及多个步骤，因此肺癌的自动分类很困难。在这篇手稿中，提出了使用不同机器学习技术的人类肺癌分类和综合分析。最初，使用肺癌数据集收集输入图像。所提出的方法使用图像处理技术处理这些图像，并利用进一步的机器学习技术进行分类。七种不同的分类器，包括 k 最近邻 (KNN)、支持向量机 (SVM)、决策树 (DT)、多项式朴素贝叶斯 (MNB)、随机梯度下降 (SGD)、随机森林 (RF) 和多层使用感知器（MLP）分类器将肺癌分类为恶性和良性。使用性能指标来检查所提出方法的性能，例如评估阳性预测值、准确性、灵敏度和 f 分数。其中，MLP分类器的性能分别比其他KNN、SVM、DT、MNB、SGD和RF高出25.34%、45.39%、15.39%、41.28%、22.17%和12.12%。研究亮点：肺癌是癌症相关死亡的主要原因。影像学（MRI、CT 和 X 射线）有助于诊断。由于复杂的成像步骤，肺癌的自动分类面临挑战。这项研究提出使用不同的机器学习技术对人类肺癌进行分类。来自肺癌数据集的输入图像经过图像处理和机器学习。 k 最近邻、支持向量机、决策树、多项朴素贝叶斯、随机梯度下降、随机森林和多层感知器 (MLP) 等分类器对癌症类型进行分类； MLP 具有出色的准确性。© 2024 Wiley periodicals LLC。

Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.© 2024 Wiley Periodicals LLC.