Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization
This article establishes the DenseNet based on multilayer network optimization (DenseNet-MNO) using a binary dataset of pneumothorax. This method optimizes multiple network layers and ensures the convergence of the neural network by reducing the learning rate with each iteration. Then, the binary cr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2024/8899192 |
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| _version_ | 1832546164083261440 |
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| author | Hongliang Huang Qike Wang Lidong Wang |
| author_facet | Hongliang Huang Qike Wang Lidong Wang |
| author_sort | Hongliang Huang |
| collection | DOAJ |
| description | This article establishes the DenseNet based on multilayer network optimization (DenseNet-MNO) using a binary dataset of pneumothorax. This method optimizes multiple network layers and ensures the convergence of the neural network by reducing the learning rate with each iteration. Then, the binary cross-entropy loss function and accuracy evaluation function iteratively evaluated the effectiveness of the deep learning model and conducted multiple experiments. The experimental results show that during the iteration process, the loss is reduced, and the accuracy is improved. The DenseNet-MNO classification model has a strong generalization ability and will not overfit. The classification accuracy is between 80% and 85%. The DenseNet-MNO classification model can accurately detect the condition of pneumothorax, providing technical support for detection technology. |
| format | Article |
| id | doaj-art-d44bb44841cc4847866543c415084349 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-d44bb44841cc4847866543c4150843492025-02-03T07:23:45ZengWileyJournal of Mathematics2314-47852024-01-01202410.1155/2024/8899192Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network OptimizationHongliang Huang0Qike Wang1Lidong Wang2School of Statistics and Data ScienceSchool of Statistics and Data ScienceSchool of Statistics and Data ScienceThis article establishes the DenseNet based on multilayer network optimization (DenseNet-MNO) using a binary dataset of pneumothorax. This method optimizes multiple network layers and ensures the convergence of the neural network by reducing the learning rate with each iteration. Then, the binary cross-entropy loss function and accuracy evaluation function iteratively evaluated the effectiveness of the deep learning model and conducted multiple experiments. The experimental results show that during the iteration process, the loss is reduced, and the accuracy is improved. The DenseNet-MNO classification model has a strong generalization ability and will not overfit. The classification accuracy is between 80% and 85%. The DenseNet-MNO classification model can accurately detect the condition of pneumothorax, providing technical support for detection technology.http://dx.doi.org/10.1155/2024/8899192 |
| spellingShingle | Hongliang Huang Qike Wang Lidong Wang Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization Journal of Mathematics |
| title | Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization |
| title_full | Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization |
| title_fullStr | Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization |
| title_full_unstemmed | Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization |
| title_short | Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization |
| title_sort | research on pneumothorax classification model of densenet based on multilayer network optimization |
| url | http://dx.doi.org/10.1155/2024/8899192 |
| work_keys_str_mv | AT honglianghuang researchonpneumothoraxclassificationmodelofdensenetbasedonmultilayernetworkoptimization AT qikewang researchonpneumothoraxclassificationmodelofdensenetbasedonmultilayernetworkoptimization AT lidongwang researchonpneumothoraxclassificationmodelofdensenetbasedonmultilayernetworkoptimization |