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Research Article | Open Access |

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) · Vol. 12, No. 2 · Published 2021 · Cited by 6

DOI: https://doi.org/10.14569/IJACSA.2021.0120296

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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

How to Cite this Article

HASSANI, A. E., SKOURT, B. A., & MAJDA, A. (2021). Efficient Lung Nodule Classification Method using Convolutional Neural Network and Discrete Cosine Transform. International Journal of Advanced Computer Science and Applications, 12(2). https://doi.org/10.14569/IJACSA.2021.0120296

HASSANI, Abdelhamid EL, et al.. "Efficient Lung Nodule Classification Method using Convolutional Neural Network and Discrete Cosine Transform." International Journal of Advanced Computer Science and Applications, vol. 12, no. 2, 2021, https://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},
  volume    = {12},
  number    = {2},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Abdelhamid EL HASSANI and Brahim AIT SKOURT and Aicha MAJDA},
  doi       = {10.14569/IJACSA.2021.0120296},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120296}
}

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