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

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.

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}
}