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Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.

Monitoring temperature changes of infants in the neonatal intensive care unit is very important. Especially for premature and very low birthweight infants, determining temperature changes in their skin immediately is extremely significant for follow-up processes. The development of medical infrared thermal imaging technologies provides accurate and contact-free measurement of body temperature. This method is used to detect thermal radiation emitted from the body to obtain skin temperature distributions. The purpose of this study is to develop an analysis system based on infrared thermal imaging to classify neonates who are healthy and suffering from heart disease using their skin temperature distribution. In this study, 258 infrared thermograms obtained applying data augmentation on 43 infrared thermograms captured from the Neonatal Intensive Care Unit were used. The following operations were performed: firstly, images were segmented to eliminate unnecessary details on the thermogram. Secondly, the features of the image were extracted applying Discrete Wavelet Transform (DWT), Ridgelet Transform (RT), Curvelet Transform (CuT), and Contourlet Transform (CoT) which are multiresolution analysis methods. Finally, these features are classified as healthy and unhealthy using classification methods such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). The best results were obtained with SVM as 96.12% of an accuracy, 94.05% of a sensitivity and 98.28% of a specificity.

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