A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble
In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/4/390 |
| _version_ | 1827343732414873600 |
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| author | Qiuyu An Wei Chen Wei Shao |
| author_facet | Qiuyu An Wei Chen Wei Shao |
| author_sort | Qiuyu An |
| collection | DOAJ |
| description | In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model’s integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model’s high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment. |
| first_indexed | 2024-03-07T22:35:30Z |
| format | Article |
| id | doaj.art-6bcc73ebd8f34b6e9147081c43a7900d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-07T22:35:30Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-6bcc73ebd8f34b6e9147081c43a7900d2024-02-23T15:13:46ZengMDPI AGDiagnostics2075-44182024-02-0114439010.3390/diagnostics14040390A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention EnsembleQiuyu An0Wei Chen1Wei Shao2School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaNanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518067, ChinaIn the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model’s integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model’s high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment.https://www.mdpi.com/2075-4418/14/4/390deep learningattention mechanismAI in healthcarepneumonia detectionX-ray image analysismedical data mining |
| spellingShingle | Qiuyu An Wei Chen Wei Shao A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble Diagnostics deep learning attention mechanism AI in healthcare pneumonia detection X-ray image analysis medical data mining |
| title | A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble |
| title_full | A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble |
| title_fullStr | A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble |
| title_full_unstemmed | A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble |
| title_short | A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble |
| title_sort | deep convolutional neural network for pneumonia detection in x ray images with attention ensemble |
| topic | deep learning attention mechanism AI in healthcare pneumonia detection X-ray image analysis medical data mining |
| url | https://www.mdpi.com/2075-4418/14/4/390 |
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