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• W3156331990 abstract "Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy." @default.
• W3156331990 created "2021-04-26" @default.
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• W3156331990 date "2021-04-20" @default.
• W3156331990 modified "2023-10-11" @default.
• W3156331990 title "AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays" @default.
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• W3156331990 doi "https://doi.org/10.7717/peerj-cs.495" @default.
• W3156331990 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8064140" @default.
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