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

    描述:
    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.
    关键词:
    Optimization, Lung Cancer, Recurrent Neural Network, and CT images
    学科:
    Data Science and Artificial Intelligence
    创造者:
    Roy, Sudipta, Gunjan, Vinit Kumar , Shaik, Fahimudin, and Singh, Ninni
    Owner:
    n.sakthivel@jioinstitute.edu.in
    出版者:
    Springer Nature
    位置:
    Cham and Switzerland
    语言:
    English
    日期上传:
    10-02-2023
    修改日期:
    16-02-2023
    创建日期:
    01-11-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    识别码:
    10.1007/s12553-022-00700-8