The shape and size of nodules are significant pointers of malignancy in cancer diagnosis. Moreover, considerable capture of nodules' structural data attained from computed tomography (CT) scans in computer-aided design is a confronting task. Various investigations deal with computationally deep ensemble approaches/convolutional neural network (CNN) models; however, sampling tumors based on multi-section-You Only Look Once (MS-YOLO) architecture, this anticipated model acquires nodules' multi-sections from various views and encodes nodule's information to data aggregation from diverse cross-sections through pooling layers. Subsequently, the features are utilized for the nodule classification task. This MS YOLO does not need any nodules' spatial annotation. However, it works directly over the cross-section acquired from enclosed nodule volume. This work has been analyzed using LUNA R16 dataset. It attains adequate performance with a mean value of 90.8% classification accuracy. Anticipated architecture is cast-off to choose cross-section determination of malignancy that helps in output interpretation. The proposed model shows better trade-off among previous methodologies. Simulation was carried out in MATLAB environment.

中文翻译：

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建模一个有效的多部分你只看一次以增强肺癌预测

结节的形状和大小是癌症诊断中恶性程度的重要指标。此外，从计算机辅助设计中的计算机断层扫描 (CT) 扫描获得的结节结构数据的大量捕获是一项艰巨的任务。各种研究涉及计算深度集成方法/卷积神经网络 (CNN) 模型；然而，基于多部分-您只看一次（MS-YOLO）架构对肿瘤进行采样，这种预期模型从各种视图获取结节的多部分，并通过池化层将结节的信息编码为来自不同横截面的数据聚合。随后，这些特征被用于结节分类任务。这个 MS YOLO 不需要任何结节的空间注释。然而，它直接作用于从封闭结节体积中获得的横截面。这项工作已使用 LUNA R16 数据集进行分析。它获得了足够的性能，平均分类准确率为 90.8%。预期的架构被抛弃以选择有助于输出解释的恶性肿瘤的横截面确定。所提出的模型显示了先前方法之间更好的权衡。仿真在MATLAB环境下进行。