Intelligent prognostic system for pediatric pneumonia based on sustainable IoHT

• Published in: Multimedia tools and applications 2023-07, Vol.82 (17), p.26901-26917
• Main Authors: Sasikaladevi, N., Revathi, A.
• Format: Article
• Language: English
• Subjects: Accuracy; Bacterial diseases; Bacterial infections; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Image quality; Lungs; Machine learning; Medical imaging; Mortality; Multimedia Information Systems; Neural networks; Pediatrics; Pneumonia; Population density; Public health; Smart cities; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-14930-z

Despite the growing impacts of environmental changes due to smart city development, sustainable Internet of Health Things (IoHT) retains improved public health. Containment of contagious diseases is one of the prime factors as the population density in the smart city environment is growing exponentially. This work focuses on the prognosis of pediatric pneumonia through the IoHT framework. In the world population, nearly 15% of children under five years of mortality are caused by a lung infection called pediatric pneumonia. It kills approximately 800 thousand children every year, and 2200 children daily mortality rate due to pediatric pneumonia. The disease is caused by viral or bacterial infections in the lungs. Chest X-ray (CXR) is the predominant method for diagnosing and severity analysis of pneumonia by pediatricians. However, the CXR images are low-quality images, demanding the intelligence for accurate analysis and interpretation. Hence, researchers developed different machine learning and deep learning methods to diagnose pneumonia from CXR images in recent years. However, it lacks the accuracy of interpretations. This paper proposes a deep transfer learning-based neural network-based IoHT framework to diagnose pneumonia due to viral and bacterial infections. The proposed model is twofold: the first is the deep transfer learning network for discriminating normal CXR from pneumonia-affected lung CXR images. The second is that the deep transfer learning network is trained by an optimized training method called Adaptive Movement Estimation and deployed in IoHT. The performance of the proposed system is analyzed in terms of accuracy, sensitivity, specificity, and Area Under the Curve (AUC). It yields the highest sensitivity of 98.2% and a precision of 98.8%. The proposed system also yields a validation accuracy of 97.88, which is high compared to other state-of-the-art transfer learning methods for diagnosing pediatric pneumonia.