Malignant Lung Nodule Detection using Deep Learning

Amrit Sreekumar, Karthika Nair, S. Sudheer, H. Ganesh Nayar, J. J. Nair
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引用次数: 17

Abstract

Lung Carcinoma, commonly known as Lung Cancer is an infectious lung tumour caused by uncontrollable tissue growth in the lungs. Presented here is an approach to detect malignant pulmonary nodules from CT scans using Deep Learning. A preprocessing pipeline was used to mask out the lung regions from the scans. The features were then extracted using a 3D CNN model based on the C3D network architecture. The LIDC-IDRI is the primary dataset used along with a few resources from the LUNA16 grand challenge for the reduction of false-positives. The end product is a model that predicts the coordinates of malignant pulmonary nodules and demarcates the corresponding areas from the CT scans. The final model achieved a sensitivity of 86 percent for detecting malignant Lung Nodules and predicting its malignancy scores.
基于深度学习的恶性肺结节检测
肺癌,俗称肺癌,是一种传染性肺部肿瘤,由肺部组织生长不可控引起。本文介绍了一种利用深度学习技术从CT扫描中检测恶性肺结节的方法。使用预处理管道从扫描中屏蔽肺部区域。然后使用基于C3D网络架构的3D CNN模型提取特征。LIDC-IDRI是用于减少误报的主要数据集,以及来自LUNA16大挑战的一些资源。最终产品是一个模型,预测恶性肺结节的坐标,并从CT扫描中划定相应的区域。最终模型在检测恶性肺结节和预测其恶性评分方面达到了86%的敏感性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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