Early Detection of Diabetes using Thermography and Artificial Neural Networks

The aim of this work is to demonstrate the usefulness of the artificial intelligence tools for early detection of diseases. From the historic and simple assessment of temperature by the clinical thermometer, thermal imaging camera has opened up new perspectives, and that a whole image field-of-view can be characterized in a single measurement. Thermographic assessment of temperature distribution within the examined skin enables a quick, non-contact, non-invasive relative measurement of their temperature. No literature has been found until date detection of diabetes using thermography and artificial neural networks. An attempt in this regard could help doctors make a safer decision. This work shows that the output predicted using the artificial neural network based on thermography, can be used for early detection of diabetes.