Classification of lung pathologies in neonates using dual-tree complex wavelet transform

• Published in: Biomedical engineering online Vol. 22; no. 1; pp. 1 - 22
• Main Authors: Aujla, Sagarjit, Mohamed, Adel, Tan, Ryan, Magtibay, Karl, Tan, Randy, Gao, Lei, Khan, Naimul, Umapathy, Karthikeyan
• Format: Journal Article
• Language: English
• Published: London BioMed Central 04.12.2023; BioMed Central Ltd; BMC
• Subjects: Automation; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Classification; Computer-aided medical diagnosis; Datasets; Developing countries; Diagnosis; Diagnosis, Ultrasonic; Discriminant analysis; Diseases; Engineering; Fatalities; Health care; Image acquisition; Image analysis; Image classification; Image processing; Infants; Infants (Newborn); Intensive care; LDCs; Lung diseases; Lungs; Medical imaging; Methods; Morphology; Neonatal lung ultrasound; Neonates; Patient outcomes; Pattern classification; Pediatric research; Respiratory distress syndrome; Ultrasonic imaging; Wavelet decomposition; Wavelet transforms; Canada; United States > US; Image analysis; Pattern classification; Neonatal lung ultrasound; Wavelet decomposition
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-023-01184-x

Introduction Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. Methods We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. Results Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. Conclusion Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.