人工智能
计算机科学
特征提取
模式识别(心理学)
杠杆(统计)
卷积神经网络
分类器(UML)
上下文图像分类
计算机视觉
图像(数学)
作者
Anum Masood,Bin Sheng,Po Yang,Ping Li,Huating Li,Jinman Kim,David Dagan Feng
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2020-12-01
卷期号:16 (12): 7791-7801
被引量:52
标识
DOI:10.1109/tii.2020.2972918
摘要
Detection of lung cancer at early stages is critical, in most of the cases radiologists read computed tomography (CT) images to prescribe follow-up treatment. The conventional method for detecting nodule presence in CT images is tedious. In this article, we propose an enhanced multidimensional region-based fully convolutional network (mRFCN) based automated decision support system for lung nodule detection and classification. The mRFCN is used as an image classifier backbone for feature extraction along with the novel multilayer fusion region proposal network (mLRPN) with position-sensitive score maps being explored. We applied a median intensity projection to leverage three-dimensional information from CT scans and introduced deconvolutional layer to adopt proposed mLRPN in our architecture to automatically select the potential region of interest. Our system has been trained and evaluated using LIDC dataset, and the experimental results showed promising detection performance in comparison to the state-of-the-art nodule detection/classification methods, achieving a sensitivity of 98.1% and classification accuracy of 97.91%.
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