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DOI: 10.14569/IJACSA.2021.0120296
PDF

Efficient Lung Nodule Classification Method using Convolutional Neural Network and Discrete Cosine Transform

Author 1: Abdelhamid EL HASSANI
Author 2: Brahim AIT SKOURT
Author 3: Aicha MAJDA

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.

  • Abstract and Keywords
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Abstract: In today’s medicine, Computer-Aided Diagnosis Systems (CAD) are very used to improve the screening test accuracy of pulmonary nodules. Processing, classification, and detection techniques form the basis of CAD architecture. In this work, we focus on the classification step in a CAD system where we use Discrete Cosine Transform (DCT) along with Convolutional Neural Network (CNN) to perform an efficient classification method for pulmonary nodules. Combining both DCT and CNN, the proposed method provides high-level accuracy that outperforms the conventional CNN model.

Keywords: Convolutional neural network; discrete cosine trans-form; pulmonary nodule classification; computer aided diagnosis systems

Abdelhamid EL HASSANI, Brahim AIT SKOURT and Aicha MAJDA, “Efficient Lung Nodule Classification Method using Convolutional Neural Network and Discrete Cosine Transform” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120296

@article{HASSANI2021,
title = {Efficient Lung Nodule Classification Method using Convolutional Neural Network and Discrete Cosine Transform},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120296},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120296},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Abdelhamid EL HASSANI and Brahim AIT SKOURT and Aicha MAJDA}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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