A Classification of benign and malignant lung nodules based on feature fusion and improved random forest
Pages 478 - 485
Abstract
To improve the accuracy of benign and malignant classification of lung nodules in CT images, a method based on feature fusion and improved random forest is proposed to classify benign and malignant lung nodules. First, the high-order features of lung nodules extracted by convolutional noise reduction self-encoder and convolutional neural network are fused; second, the random forest model is optimized by two-step chi-square test. Experiments on benign and malignant classification were conducted using 2000 lung nodule samples in the LIDC-IDRI dataset. The experimental results show that the proposed method has high performance in classifying benign and malignant lung nodules, with accuracy, sensitivity, and specificity of 0.9566, 0.9524 and 0.9626, respectively. Compared with the original random forest, the number of decision trees was reduced by 80% and the accuracy was improved by 0.0144.
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- A Classification of benign and malignant lung nodules based on feature fusion and improved random forest
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Published In
August 2024
967 pages
ISBN:9798400717826
DOI:10.1145/3706890
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Association for Computing Machinery
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Published: 13 January 2025
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ISAIMS 2024
ISAIMS 2024: 2024 5th International Symposium on Artificial Intelligence for Medicine Science
August 13 - 17, 2024
Amsterdam, Netherlands
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Overall Acceptance Rate 53 of 112 submissions, 47%
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