肺癌是全球第二大常见癌症，每年新增病例超过 200 万。早期识别将使医疗保健从业者能够更有效地处理它。计算机辅助检测系统的进步极大地影响了人类疾病的临床分析和决策。为此，机器学习和深度学习技术正在成功应用。由于多种优点，迁移学习已在基于图像数据的疾病检测中变得流行。在这项工作中，我们通过堆叠三种不同的迁移学习模型构建了一种新颖的迁移学习模型（VER-Net），以使用肺部 CT 扫描图像检测肺癌。该模型经过训练，可以绘制四种肺癌类别的 CT 扫描图像。采取了图像预处理、数据增强和超参数调整等各种措施来提高 VER-Net 的效率。所有模型均使用胸部 CT 图像多类分类进行训练和评估。实验结果证实 VER-Net 优于其他八种迁移学习模型。在准确率、精确率、召回率和 F1 分数测试中，VER-Net 的得分分别为 91%、92%、91% 和 91.3%。与最先进的技术相比，VER-Net 具有更好的准确性。VER-Net 不仅可以有效地用于肺癌检测，而且还可能对其他可获得 CT 扫描图像的疾病有用。© 2024。作者。

Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data.In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images.The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy.VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.© 2024. The Author(s).