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dc.contributor.authorFan, Rong
dc.contributor.authorBu, Shengrong
dc.date.accessioned2022-03-02T16:35:34Z
dc.date.available2022-03-02T16:35:34Z
dc.date.issued2022
dc.identifier.citationEntropy 2022, 24(3), 313en_US
dc.identifier.issn1099-4300
dc.identifier.urihttp://hdl.handle.net/10464/15642
dc.description.abstractUsing chest X-ray images is one of the least expensive and easiest ways to diagnose patients who suffer from lung diseases such as pneumonia and bronchitis. Inspired by existing work, a deep learning model is proposed to classify chest X-ray images into 14 lung-related pathological conditions. However, small datasets are not sufficient to train the deep learning model. Two methods were used to tackle this: (1) transfer learning based on two pretrained neural networks, DenseNet and ResNet, was employed; (2) data were preprocessed, including checking data leakage, handling class imbalance, and performing data augmentation, before feeding the neural network. The proposed model was evaluated according to the classification accuracy and receiver operating characteristic (ROC) curves, as well as visualized by class activation maps. DenseNet121 and ResNet50 were used in the simulations, and the results showed that the model trained by DenseNet121 had better accuracy than that trained by ResNet50.en_US
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.subjectTransfer learningen_US
dc.subjectDeep learningen_US
dc.subjectPretrained neural networksen_US
dc.subjectChest X-ray imagesen_US
dc.subjectLung diseasesen_US
dc.titleTransfer-Learning-Based Approach for the Diagnosis of Lung Diseases from Chest X-ray Imagesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/e24030313
refterms.dateFOA2022-03-02T16:35:35Z


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