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Optimal lung cancer detection based on CNN optimized and improved Snake optimization algorithm
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AI
DOI：10.1016/j.bspc.2023.105319.png)
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摘要
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Lung cancer is an ailment that lessens the patient's capability to breathe because of the excessive spread of lung tissue cells. The cancer spreads after genetic damage to DNA. This genetic damage results in the excessive distribution and proliferation of cells, which ultimately creates a mass called a tumor. Using automatic machine vision techniques is an effective method for lung cancer detection. A novel lung cancer detection methodology for the automatic identification of lung cancer in CT images is proposed in this work This technique is developed by convolutional neural networks (CNNs). Here, the lung cancer images are first preprocessed before being included in the designed CNN. To provide optimal CNN design, a novel modified version of Snake Optimizer is designed and applied to the CNN structure. The proposed model is then tested against the IQ-OTH/NCCD-Lung Cancer Dataset. This strategy is also validated by comparing it with some different published methods in the literature. The findings demonstrated that the offered strategy outperformed the others.
Keyword:
Lung cancer
Automatic diagnosis
Convolutional neural networks
improved version of the Snake optimization
algorithm
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
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