PDF(Pubmed)
Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：肺癌是全球第二常见的癌症，每年有超过200万例新病例。早期识别将使医疗保健从业者更有效地处理它。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。由于几个优点，迁移学习已经成为基于图像数据的疾病检测的热点。
方法：在这项工作中，我们通过堆叠三种不同的迁移学习模型来建立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。训练该模型以将CT扫描图像与四个肺癌类别映射。各种措施,如图像预处理，数据增强，和超参数调整，是为了提高VER-Net的功效。使用多分类胸部CT图像对所有模型进行训练和评估。
结果：实验结果证实，与其他八种迁移学习模型相比，VER-Net的表现优于其他八种迁移学习模型。VER-Net得分91%，92%,91%,和91.3%时，测试的准确性，精度,召回，和F1得分，分别。与最先进的相比，VER-Net具有更好的准确性。
结论：VER-Net不仅可有效用于肺癌检测，而且还可用于CT扫描图像可用的其他疾病。
方法：在这项工作中，我们通过堆叠三种不同的迁移学习模型来建立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。训练该模型以将CT扫描图像与四个肺癌类别映射。各种措施,如图像预处理，数据增强，和超参数调整，是为了提高VER-Net的功效。使用多分类胸部CT图像对所有模型进行训练和评估。
结果：实验结果证实，与其他八种迁移学习模型相比，VER-Net的表现优于其他八种迁移学习模型。VER-Net得分91%，92%,91%,和91.3%时，测试的准确性，精度,召回，和F1得分，分别。与最先进的相比，VER-Net具有更好的准确性。
结论：VER-Net不仅可有效用于肺癌检测，而且还可用于CT扫描图像可用的其他疾病。
