A New Hybrid CNN-LSTM Model for the X-Ray Image-Based Detection of Paediatric Croup Cough Disease
Keywords:
COVID-19, CNN, pertinent, achieves, alternative, insufficientAbstract
The importance of computer-based illness detection has grown recently, especially in the medical field where radiological imaging methods like X-ray, CT, and MRI are essential for identifying a wide range of ailments. The development of the Coronavirus (COVID-19) has tested the limits of medical innovation. Due to the similarity in symptoms, it can be difficult for doctors to differentiate COVID-19 from respiratory diseases such croup, bronchitis, and laryngitis. The primary age range for children who contract croup is between six months and three years old. However, croup and COVID-19 symptoms frequently coincide. Due to the insufficient availability of pertinent information in medical pictures, x-ray-based croup detection is not frequently used, therefore an improved computer-based detection method offers a viable alternative for early diagnosis. In this context, we suggest a hybrid model for the categorization of croup cough that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The LSTM is used for classification, whereas the CNN is used for feature extraction. Based on the experimental findings, the combined CNN-LSTM model, although being trained on a very short dataset, achieves an amazing testing accuracy of 97.66%.
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