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Transfer learning approach for pediatric pneumonia diagnosis using channel attention deep CNN architectures.
- Source :
-
Engineering Applications of Artificial Intelligence . Aug2023:Part B, Vol. 123, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Chest X-ray is the most commonly adopted non-invasive and painless diagnostic test for pediatric pneumonia. However, the low radiation levels for diagnosis make accurate detection challenging, and this initiates the need for an unerring computer-aided diagnosis model. Our work proposes stacking ensemble learning on features extracted from channel attention deep CNN architectures. The features extracted from the channel attention-based ResNet50V2, ResNet101V2, ResNet152V2, Xception, and DenseNet169 are individually passed through Kernel PCA for dimensionality reduction and concatenated. A stacking classifier with Support Vector Classifier, Logistic Regression, K-Nearest Neighbour, Nu-SVC, and XGBClassifier is employed for the final- Normal and Pneumonia classification. The stacking classifier achieves an accuracy of 96.15%, precision of 97.91%, recall of 95.90%, F1 score of 96.89%, and an AUC score of 96.24% on the publicly available pediatric pneumonia dataset. We expect this model to help the real-time diagnosis of pediatric pneumonia significantly. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DEEP learning
*COMPUTER-aided diagnosis
*NONINVASIVE diagnostic tests
*PNEUMONIA
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 123
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 164089475
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106416