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  • 1. Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network

    Descrizione:
    Purpose  For radiologists, identifying and assessing thelung nodules of cancerous form from CT scans is a difficult and laborious task. As a result, early lung growing prediction is required for the investigation technique, and hence it increases the chances of a successful treatment. To ease this problem, computer-aided diagnostic (CAD) solutions have been deployed. The main purpose of the work is to detect the nodules are malignant or not and to provide the results with better accuracy. Methods  A neural network model that incorporates a feedback loop is the recurrent neural network. Evolutionary algorithms such as the Grey Wolf Optimization Algorithm and Recurrent Neural Network (RNN) Techniques are investigated utilising the Matlab Tool in this work. Statistical attributes are also produced and compared with other RNN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)combinations for study. Results  The proposed method produced very high accuracy, sensitivity, specificity, and precision and compared with other state of art methods. Because of its simplicity and possible global search capabilities, evolutionary algorithms have shown tremendous promise in the area of feature selection in the latest years. Conclusion  The proposed techniques have demonstrated outstanding outcomes in various disciplines, outperforming classical methods. Early detection of lung nodules will aid in determining whether the nodules will become malignant or not.
    Parola chiave:
    Optimization, Lung Cancer, Recurrent Neural Network, and CT images
    Soggetto:
    Data Science and Artificial Intelligence
    Creatore:
    Roy, Sudipta, Gunjan, Vinit Kumar , Shaik, Fahimudin, and Singh, Ninni
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Editore:
    Springer Nature
    luogo:
    Cham and Switzerland
    Lingua:
    English
    Data caricata:
    10-02-2023
    Data modificata:
    16-02-2023
    data di creazione:
    01-11-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identifier:
    10.1007/s12553-022-00700-8