Accurate detection of malignant tumor on lung computed tomography scans is crucial for early diagnosis of lung cancer and hence the faster recovery of patients. Several deep learning methodologies have been proposed for lung tumor detection, especially the convolution neural network (CNN). However, as CNN may lose some of the spatial relationships between features, we plan to combine texture features such as fractal features and gray-level co-occurrence matrix (GLCM) features along with the CNN features to improve the accuracy of tumor detection. Our framework has two advantages. First it fuses the advantage of CNN features with hand-crafted features such as fractal and GLCM features to gather the spatial information. Second, we reduce the overfitting effect by replacing the softmax layer with the support vector machine classifier. Experiments have shown that texture features such as fractal and GLCM when concatenated with deep features extracted from DenseNet architecture have a better accuracy of 95.42%, sensitivity of 97.49%, and specificity of 93.97%, and a positive predictive value of 95.96% with area under curve score of 0.95.

中文翻译：

---

使用深度、分形和灰度共现矩阵特征的组合预测肺结节的恶性肿瘤

在肺部CT扫描中准确检测恶性肿瘤对于肺癌的早期诊断和患者更快的康复至关重要。已经提出了几种用于肺肿瘤检测的深度学习方法，尤其是卷积神经网络（CNN）。然而，由于 CNN 可能会丢失一些特征之间的空间关系，我们计划将纹理特征（如分形特征和灰度共生矩阵（GLCM）特征）与 CNN 特征结合起来，以提高肿瘤检测的准确性。我们的框架有两个优点。首先，它将 CNN 特征的优势与分形和 GLCM 特征等手工制作的特征相融合，以收集空间信息。其次，我们通过用支持向量机分类器替换 softmax 层来减少过拟合效应。