Abstract:
: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.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Srivastava V,Purwar RKdoi
10.1007/s10278-019-00245-9subject
Has Abstractpub_date
2020-02-01 00:00:00pages
252-261issue
1eissn
0897-1889issn
1618-727Xpii
10.1007/s10278-019-00245-9journal_volume
33pub_type
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