Thermogram classification using deep siamese network for neonatal disease detection with limited data. Issue 5 (20th October 2022)

• Record Type: Journal Article
• Authors: Ervural, Saim; Ceylan, Murat
• Is Part Of: Quantitative infrared thermography. Volume 19:Issue 5(2022)
• Page Start: 312
• Page End: 330
• Publication Date: 2022-10-20
• Subjects: Disease classification -- neonatal intensive care -- one-shot learning -- siamese neural network -- thermal imaging; Infrared technology -- Periodicals; Thermography -- Periodicals; Infrared technology; Thermography; Periodicals; 621.362
• DOI: 10.1080/17686733.2021.2010379
• Languages: English
• ISSNs: 1768-6733

Abstract

ABSTRACT: Monitoring the body temperatures and evaluating the thermal asymmetry of newborns give an idea about neonatal diseases. Infrared thermography is a non-invasive, non-harmful, and non-contact modality that allows the monitoring of the body temperature distribution. Early diagnosis using a limited data set is extremely vital due to the high mortality rate in newborns and some difficulties in neonatal imaging. Thermography stands out as a useful tool in detecting neonatal diseases compared to other techniques. However, creating a thermogram database consisting of thousands of images from each class required by traditional artificial intelligence methods, is impossible due to the sensitivity of newborns. One of the meta-learning models that has recently gained success in applying limited data learning, especially one-shot, in various fields is Siamese neural networks. In this work, we perform a multi-class classification to provide pre-diagnosis to experts in disease detection using Siamese neural networks. By using two different optimisation techniques and data augmentation, critical diseases with only a few sample data are classified using the method tested in two- and three-class evaluation approaches. The results based on the disease type achieve 99.4% accuracy in infection diseases and 96.4% oesophageal atresia, 97.4% in intestinal atresia, and 94.02% in necrotising enterocolitis.