Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks
| Autor: | Jose Vigno Moura Sousa, D. B. S. Santos, Salviano Soares, Arata Andrade Saraiva, Nuno M. F. Ferreira, Nator Junior C. Costa, António Valente |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
business.industry Computer science Computer Science::Neural and Evolutionary Computation 020206 networking & telecommunications Pattern recognition 02 engineering and technology medicine.disease Convolutional neural network Data set Pneumonia Multilayer perceptron 0202 electrical engineering electronic engineering information engineering X ray image medicine 020201 artificial intelligence & image processing Artificial intelligence business Classification of pneumonia |
| Zdroj: | BIOIMAGING |
| DOI: | 10.5220/0007346600760083 |
| Popis: | This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network. |
| Databáze: | OpenAIRE |
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