Purpose

Lung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification approach named bilinear convolutional neural network (BCNN) for the classification of lung nodules on CT images.

Methods

Convolutional neural network (CNN) is considered as the leading model in deep learning and is highly recommended for the design of computer-aided diagnosis systems thanks to its promising results on medical image analysis. The proposed BCNN scheme consists of two-stream CNNs (VGG16 and VGG19) as feature extractors followed by a support vector machine (SVM) classifier for false positive reduction. Series of experiments are performed by introducing the bilinear vector features extracted from three BCNN combinations into various types of SVMs that we adopted instead of the original softmax to determine the most suitable classifier for our study.

Results

The method performance was evaluated on 3186 images from the public LUNA16 database. We found that the BCNN [VGG16, VGG19] combination with and without SVM surpassed the [VGG16]2 and [VGG19]2 architectures, achieved an accuracy rate of 91.99% against 91.84% and 90.58%, respectively, and an area under the curve (AUC) rate of 95.9% against 94.8% and 94%, respectively.

Conclusion

The proposed method improved the outcomes of conventional CNN-based architectures and showed promising and satisfying results, compared to other works, with an affordable complexity. We believe that the proposed BCNN can be used as an assessment tool for radiologists to make a precise analysis of lung nodules and an early diagnosis of lung cancers.

中文翻译：

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用于CT图像上肺结节分类的双线性卷积神经网络

目的

肺癌是世界上最常见的癌症，并且是与癌症相关的死亡的主要原因。其在肺结节阶段的早期发现和治疗可改善预后。在这项研究中提出了一种新的分类方法，称为双线性卷积神经网络（BCNN），用于在CT图像上对肺结节进行分类。

方法

卷积神经网络（CNN）被认为是深度学习中的领先模型，由于其在医学图像分析方面的可喜成果，因此强烈推荐用于计算机辅助诊断系统的设计。所提出的BCNN方案由两个流CNN（VGG16和VGG19）作为特征提取器，然后是支持向量机（SVM）分类器，用于假阳性减少。通过将从三种BCNN组合中提取的双线性矢量特征引入我们所采用的各种类型的SVM（而不是原始的softmax）中来确定最适合我们的分类器的方式，进行了一系列实验。

结果

在来自公共LUNA16数据库的3186张图像上评估了方法的性能。我们发现，带有和不带有SVM的BCNN [VGG16，VGG19]组合都超过了[VGG16] 2和[VGG19] 2架构，准确率分别为91.99％，91.84％和90.58％，并且曲线下的面积（AUC）率分别为95.9％和94.8％和94％。

结论

与其他工作相比，所提出的方法改善了传统基于CNN的体系结构的结果，并显示出令人鼓舞和令人满意的结果，并且具有可承受的复杂性。我们认为，建议的BCNN可以用作放射科医生的评估工具，以准确分析肺结节和早期诊断肺癌。