Stacking algorithm based on naive Bayes
Article No.: 14, Pages 1 - 6
Abstract
With the advent of the data era, medical image data plays an increasingly important role in diagnosing and treating diseases. This study combined deep learning, ensemble learning, and mathematical theory to construct a Stacking algorithm model based on the Naive Bayes method to achieve more accurate classification of medical images. In the Stacking algorithm, the results of the primary classifier are treated equally, and the performance of the primary classifier is ignored. The poor performance of the primary classifier will affect the final classification result. To solve this problem, a Stacking algorithm based on the Naive Bayes method is proposed in this paper. Firstly, the performance of the primary classifier is evaluated, and the output of the primary classifier is selected reasonably through the evaluation results. Secondly, the VGGNet, InceptionNet, and ResNet convolutional networks were used as primary classifiers to construct a complete Stacking algorithm based on the Naive Bayes method. Next, the algorithm was tested and validated using COVID-19 and NoCOVID-19 lung CT image data. Finally, it is compared with a single primary classifier and the traditional Stacking algorithm. The experimental results show that the Stacking algorithm based on the Naive Bayes method is suitable for the binary classification task of medical images and has better classification results than a single primary classifier and the traditional Stacking algorithm.
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Published In
April 2024
373 pages
ISBN:9798400716607
DOI:10.1145/3663976
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 June 2024
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- Research-article
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- Refereed limited
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- Key R&D Program of the Scientific Research Department
- Beijing Natural Science Foundation
- National Natural Science Foundation of China
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CVIPPR 2024
CVIPPR 2024: 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 26 - 28, 2024
Xiamen, China
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Overall Acceptance Rate 14 of 38 submissions, 37%
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