A deadly disease that affects people in various countries in the world is Lung Cancer (LC). The rate at which people die due to LC is high because it cannot be detected easily at its initial stage of tumor development. The lives of many people who are affected by LC are assured if it is detected in the initial stage. The diagnosis of LC is possible with conventional Computer-Aided Diagnosis (CAD). The process of diagnosis can be improved by providing the associated evaluation outcomes to the radiologists. Since the results from the process of extraction of features and segmentation of lung nodule are crucial in determining the operation of the traditional CAD system, the results from the CAD system highly depends on these processes. The LC classification from Computed Tomography (CT) images of three dimensions (3D) using a CAD system is the key aspect of this paper. The collection of the 3D-CT images from the standard data source takes place in the first stage. The obtained images are provided as input for the segmentation stage, in which a Multi-scale 3D TransUNet (M-3D-TUNet) is adopted to get the precise segmentation of the LC images. A multi-cascaded model that incorporates Residual Network (ResNet), Visual Geometry Group (VGG)-19, and DenseNet models is utilized to obtain the deep features from the segmented images. The segmented image from the M-3D-TUNet model is given as input to this multi-cascaded network. The features are obtained and fused to form the feature pool. The feature pool features are provided to the Enhanced Long Short Term Memory with Attention Mechanism (ELSTM-AM) for classification of the LC. The ELSTM-AM classifies the images as normal or healthy

中文翻译：

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基于 3D CT 图像的多尺度 3D TransUNet 辅助肿瘤分割和多级联模型用于肺癌诊断系统并形成融合特征池

肺癌（LC）是一种影响世界各国人民的致命疾病。LC 导致的死亡率很高，因为它在肿瘤发展的初始阶段不易被检测到。如果在初始阶段就发现了LC，许多受LC影响的人的生命就可以得到保证。LC 的诊断可以通过传统的计算机辅助诊断 (CAD) 进行。通过向放射科医生提供相关的评估结果可以改进诊断过程。由于肺结节特征提取和分割过程的结果对于决定传统CAD系统的操作至关重要，因此CAD系统的结果高度依赖于这些过程。使用 CAD 系统对三维 (3D) 计算机断层扫描 (CT) 图像进行 LC 分类是本文的关键部分。第一阶段从标准数据源收集 3D-CT 图像。获得的图像作为分割阶段的输入，其中采用多尺度3D TransUNet（M-3D-TUNet）来获得LC图像的精确分割。利用结合残差网络（ResNet）、视觉几何组（VGG）-19和DenseNet模型的多级联模型来从分割图像中获取深层特征。来自 M-3D-TUNet 模型的分割图像作为该多级联网络的输入。获取特征并融合形成特征池。特征池特征被提供给具有注意机制的增强型长短期记忆（ELSTM-AM）以用于LC的分类。ELSTM-AM 将图像分类为正常或健康