Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification
Kamel Hussein Rahouma, Shahenda Mahmoud Mabrouk, Mohamed Aouf
Abstract
Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.
Keywords
Convolutional neural network; Deep neural networks; Dense block; Evolutionary algorithm; Genetic algorithm; Lung nodule classification
DOI:
https://doi.org/10.11591/eei.v13i3.6828
Refbacks
There are currently no refbacks.
This work is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License .
<div class="statcounter"><a title="hit counter" href="http://statcounter.com/free-hit-counter/" target="_blank"><img class="statcounter" src="http://c.statcounter.com/10241695/0/5a758c6a/0/" alt="hit counter"></a></div>
Bulletin of EEI Stats
Bulletin of Electrical Engineering and Informatics (BEEI) ISSN: 2089-3191, e-ISSN: 2302-9285 This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) .