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首页> 外文期刊>Intelligent decision technologies >Deep learning the features maps for automated tumor grading of lung nodule structures using convolutional neural networks
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Deep learning the features maps for automated tumor grading of lung nodule structures using convolutional neural networks

机译：深入学习使用卷积神经网络的肺结节结构自动肿瘤分级的特征图

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Accurately identifying the exact boundary region of the pulmonary nodules in lung cancer images are the most challenging tasks in the Computer Aided Diagnosing schemes (CADx). Detecting the boundaries from different nodule structures is crucial due to the presence of similar visualization characteristics between the nodules and its surroundings. The study proposed an approach for pulmonary nodule region of interest (NROI) detection and segmentation using Computed Tomography (CT) lung images. Lung nodule CT images are acquired from the Lung Image Database Consortium (LIDC) public repository having 1018 cases. In this paper, a methodology for automated tumor grading of pulmonary lung nodules is proposed using Convolutional Neural Network (CNN). The salient features of benign and malignant nodules from different nodule structures are automatically self-learned and classified based on the classification strategy. The stages involved in the methodology are: 1) Pre-processing the image datasets using discrete wavelet transforms (DWT). 2) NROI segmentation. 3) NROI Feature extraction using CNN. 4) Nodule classification. CNN are trained with self-learned extracted features from NROI and are further classified as benign or malignant. Analyzing and segregating these extracted features plays a vital role in the correct classification of malignancy levels. The methodology is compared with conventional state-of-art methods and traditional hand-crafted methods. A total of 710 pulmonary nodules are used in the study, with 258 benign samples and 452 malignant samples. A consistent behavior was observed using CNN with reduced low false positives and a classification accuracy of 96.5%, sensitivity of 96%, specificity of 96.55% and standard Receiver operating characteristic (ROC) curve with the highest value of 0.969 was recorded.
机译：准确地识别肺癌图像中肺结核的精确边界区域是计算机辅助诊断方案（CADX）中最具挑战性的任务。由于结节和周围环境之间存在类似的可视化特性，检测来自不同结节结构的边界是至关重要的。该研究提出了使用计算机断层扫描（CT）肺图像的肺结结（NROI）检测和分割的方法。从肺图像数据库联盟（LIDC）公共存储库中获取肺结核CT图像，具有1018个案例。本文采用卷积神经网络（CNN）提出了一种用于肺肺结节自动肿瘤分级的方法。来自不同结节结构的良性和恶性结节的显着特征是根据分类策略自动自学习和分类。方法中涉及的阶段是：1）使用离散小波变换（DWT）预处理图像数据集。 2）NROI分割。 3）使用CNN的NRO特征提取。 4）结核分类。 CNN受到来自Nroi的自学习提取特征的培训，并进一步归类为良性或恶性。分析和隔离这些提取的特征在对恶性水平的正确分类中起着至关重要的作用。将方法与传统的现有方法和传统的手工制作方法进行比较。该研究中共使用了710个肺结节，258个良性样品和452个恶性样品。使用CNN观察到一致的行为，CNN具有降低的低误报，分类精度为96.5％，灵敏度为96％，特异性为96.55％，标准接收器操作特性（ROC）曲线具有最高值0.969的曲线。

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