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- W4307649779 abstract "Early detection of pneumonia and COVID-19 is extremely vital in order to guarantee timely access to medical treatment. Hence, it is necessary to detect pneumonia/COVID-19 from the X-ray images. In this paper, convolutional neural networks along with transfer learning are used to aid in the detection of the disease. A CNN model is proposed with four convolutional layers with four max pooling layers, one flatten layer followed by one fully connected hidden layer and output layer. Pre-trained models, namely AlexNet, InceptionV3, ResNet50, and VGG19 are implemented. Chest X-ray images (pneumonia), chest X-ray (COVID-19 and pneumonia), and COVID-19 radiography database are used for implementation for all the models. Precision, recall, and accuracy are used as performance evaluation metrices. The performance of all the models are compared. Experimental results show that the proposed CNN model outperforms all pre-trained models with improved accuracy with reduced trainable parameters. The highest accuracy achieved across all three datasets is 94.25% for the chest X-ray (COVID-19 and pneumonia) dataset." @default.
- W4307649779 created "2022-11-04" @default.
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- W4307649779 date "2022-10-28" @default.
- W4307649779 modified "2023-09-27" @default.
- W4307649779 title "Detection of Pneumonia and COVID-19 from Chest X-Ray Images Using Neural Networks and Deep Learning" @default.
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- W4307649779 doi "https://doi.org/10.1007/978-981-19-4863-3_6" @default.
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