Ervural, SaimCeylan, Murat2022-01-302022-01-3020221768-67332116-7176https://doi.org/10.1080/17686733.2021.2010379https://hdl.handle.net/20.500.13091/1697Monitoring 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.eninfo:eu-repo/semantics/closedAccessDisease ClassificationNeonatal Intensive CareOne-Shot LearningSiamese Neural NetworkThermal ImagingLow-Birth-WeightInfrared ThermographyNatural-HistorySystemThermoregulationAnomaliesInfantsArticle10.1080/17686733.2021.20103792-s2.0-85122016157