Abstract

Convolutional neural network (CNN) is one of the deep structured algorithms widely applied to analyze the ability to visualize and extract the hidden texture features of image datasets. The study aims to automatically extract the self-learned features using an end-to-end learning CNN and compares the results with the conventional state-of-art and traditional computer-aided diagnosis system’s performance. The architecture consists of eight layers: one input layer, three convolutional layers and three sub-sampling layers intercepted with batch normalization, ReLu and max-pooling for salient feature extraction, and one fully connected layer that uses softmax function connected to 3 neurons as output layer, classifying an input image into one of three classes categorized as nodules \(\ge\) 3 mm as benign (low malignancy nodules), malignant (high malignancy nodules), and nodules < 3 mm and non-nodules \(\ge\) 3 mm combined as non-cancerous. For the input layer, lung nodule CT images are acquired from the Lung Image Database Consortium public repository having 1018 cases. Images are pre-processed to uniquely segment the nodule region of interest (NROI) in correspondence to four radiologists’ annotations and markings describing the coordinates and ground-truth values. A two-dimensional set of re-sampled images of size 52 \(\times\) 52 pixels with random translation, rotation, and scaling corresponding to the NROI are generated as input samples. In addition, generative adversarial networks (GANs) are employed to generate additional images with similar characteristics as pulmonary nodules. CNNs are trained using images generated by GAN and are fine-tuned with actual input samples to differentiate and classify the lung nodules based on the classification strategy. The pre-trained and fine-tuned process upon the trained network’s architecture results in aggregate probability scores for nodule detection reducing false positives. A total of 5188 images with an augmented image data store are used to enhance the performance of the network in the study generating high sensitivity scores with good true positives. Our proposed CNN achieved the classification accuracy of 93.9%, an average specificity of 93%, and an average sensitivity of 93.4% with reduced false positives and evaluated the area under the receiver operating characteristic curve with the highest observed value of 0.934 using the GAN generated images.

中文翻译：

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使用卷积神经网络进行基于ROI的特征学习以进行有效的阳性阳性预测以进行肺癌诊断

摘要

卷积神经网络（CNN）是广泛用于分析可视化和提取图像数据集隐藏纹理特征的能力的深度结构化算法之一。这项研究旨在使用端到端的CNN自动提取自学特征，并将结果与​​常规的最新技术水平和传统的计算机辅助诊断系统的性能进行比较。该体系结构由八层组成：一个输入层，三个卷积层和三个子采样层，通过批量归一化，ReLu和最大池进行截取，以进行显着特征提取；以及一个完全连接的层，该层使用softmax函数连接到3个神经元作为输出层，将输入图像分为三类，分别为结节\（\ ge \） 3毫米为良性（低恶性结节），恶性（高恶性结节）和结节<3mm与非结核\（\ GE \）  3毫米组合成非癌。对于输入层，从具有1018个病例的肺图像数据库协会公共存储库中获取肺结节CT图像。图像经过预处理，以与四位放射科医生的注释和描述坐标和地面真实值的标记相对应，对感兴趣的结节区域（NROI）进行唯一的分割。尺寸为52 \（\ times \）的二维重新采样图像集  生成与NROI对应的具有随机平移，旋转和缩放的52个像素作为输入样本。此外，采用生成对抗网络（GAN）生成具有与肺结节相似特征的其他图像。使用GAN生成的图像训练CNN，并根据实际输入样本进行微调，以基于分类策略对肺结节进行区分和分类。经过训练的网络体系结构上的预训练和微调过程可为结节检测提供合计的概率得分，从而减少误报。总共5188张具有增强图像数据存储库的图像用于增强研究中网络的性能，从而生成具有良好真实阳性结果的高灵敏度评分。我们提出的CNN达到了93.9％的分类精度，