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A deep learning approach to differentiate between acute asthma and bronchitis in preschool children
(Institute of Electrical and Electronics Engineers Inc., 2022) Salih, Waleed; Koyuncu, Hakan
Acute asthma and bronchitis are common diseases and spread rapid that affecting children all around the world, especially in preschool children (under six years), most people confuse between the two diseases due overlapping symptoms, where it is difficult to diagnose cases by junior doctors in the hospitals and the wrong diagnosis sometimes leads to death, the main objective of this study is the right diagnosis of cases and differentiation between them, to preserve the lives of children and reduce the spread of disease which saves effort, time and money for the health institutions, in this study we will present two deep learning models for binary classification of real dataset that collected in the teaching hospital for children afflicted, which was examined by a pediatrician consultant, the final results showed that the convolutional neural network model (CNN) outperformed with 99.350% accuracy to the second classifier the long short-term memory model (LSTM), thus the CNN was adopted as a binary classification model for our study.