Thermogram classification using deep siamese network for neonatal disease detection with limited data

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.

KEYWORDS:

• Disease classification
• neonatal intensive care
• one-shot learning
• siamese neural network
• thermal imaging

Acknowledgments

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 215E019).

Highlights

1. Monitoring of thermal asymmetry in newborns give an idea about neonatal diseases

2. Neonatal disease classification with limited thermography data

3. Good classification performance with Siamese network on thermal image

4. Effects of data augmentation on limited thermal dataset

5. By thermography and AI, possible to early diagnosis and controls the treatment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Scientific and Technological Research Council of Turkey [215E019].

Notes on contributors

Saim Ervural

Murat Ceylan received his PhD. from Selcuk University, Institute of Natural Sciences, Electrical-Electronics Engineering. He currently teaches at Konya Technical University with the title of Associate Professor. His scientific work is mainly on artificial intelligence solutions using image processing in the biomedical field. He is also the CEO of AIVISIONTECH, a company that produces innovation in health.

Saim Ervural received his PhD. in Electrical and Electronics Engineering from Konya Technical University, Institute of Science and Sciences. He has studies on classification approaches with a limited data set. He currently teaches at Department of Electrical and Electronics of KTO Karatay University, Engineering and Natural Sciences Faculty. His interests are artificial intelligence and image processing in health sciences.