University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16C, New Delhi, 110075, India.
J Digit Imaging. 2020 Feb;33(1):252-261. doi: 10.1007/s10278-019-00245-9.
In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.
本文提出了一种简化而高效的深度卷积神经网络架构，用于肺部图像分类。用于分类的图像是从两个公开提供的科学使用的数据库中获得的计算机断层扫描（CT）扫描图像。还确定了六个基于外部形状的特征，即实性、圆形度、径向长度（RL）函数的离散傅里叶变换、方向梯度直方图（HOG）、矩和活动轮廓图像的直方图，并将其嵌入到所提出的卷积神经网络中。性能是根据平均召回率和平均精度值来衡量的，并与用于生物医学图像分类的六种类似方法进行了比较。对于两个数据库，所提出的系统的平均精度值为 95.26%，平均召回值为 69.56%。
