A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

Erdogan Yıldırım A., Canayaz M.

Biocybernetics and Biomedical Engineering, vol.43, no.4, pp.635-655, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 4
  • Publication Date: 2023
  • Doi Number: 10.1016/j.bbe.2023.08.004
  • Journal Name: Biocybernetics and Biomedical Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, INSPEC
  • Page Numbers: pp.635-655
  • Keywords: Chest X-ray, Deep learning, Diagnosis of disease, Neonatal, Pediatric pneumonia, Respiratory disorders
  • Van Yüzüncü Yıl University Affiliated: Yes


In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.