Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.
In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to hig...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317450 |
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| author | Honghua Liu Mingwei Zhao Chang She Han Peng Mailan Liu Bo Li |
| author_facet | Honghua Liu Mingwei Zhao Chang She Han Peng Mailan Liu Bo Li |
| author_sort | Honghua Liu |
| collection | DOAJ |
| description | In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning. This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. In the first stage, the model is trained using classical gradient descent, and relevant features are extracted; in the second stage, an objective function that minimizes the false positive rate is constructed to obtain a network model with high accuracy and low false positive rate. Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. The results show that the model has made significant progress, with the false positive rate reduced to 11.3% and 7.5%, and the area under the ROC curve increased to 92.8% and 97.01%. |
| format | Article |
| id | doaj-art-b96db6ca81354cdbae9bd1fa155b294f |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b96db6ca81354cdbae9bd1fa155b294f2025-02-05T05:32:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031745010.1371/journal.pone.0317450Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.Honghua LiuMingwei ZhaoChang SheHan PengMailan LiuBo LiIn 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning. This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. In the first stage, the model is trained using classical gradient descent, and relevant features are extracted; in the second stage, an objective function that minimizes the false positive rate is constructed to obtain a network model with high accuracy and low false positive rate. Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. The results show that the model has made significant progress, with the false positive rate reduced to 11.3% and 7.5%, and the area under the ROC curve increased to 92.8% and 97.01%.https://doi.org/10.1371/journal.pone.0317450 |
| spellingShingle | Honghua Liu Mingwei Zhao Chang She Han Peng Mailan Liu Bo Li Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. PLoS ONE |
| title | Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. |
| title_full | Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. |
| title_fullStr | Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. |
| title_full_unstemmed | Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. |
| title_short | Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. |
| title_sort | classification of ct scan and x ray dataset based on deep learning and particle swarm optimization |
| url | https://doi.org/10.1371/journal.pone.0317450 |
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