Computerized detection of XXXX with artificial intelligence and thermograms.

@entity1 @entity1558 @entity1274 @entity0
This paper shows the concurrent use of thermography and artificial @entity1274 (ANN) for the diagnosis of @entity0 , a disease that is growing in prominence in @entity1 all over the world. It has been reported that breast thermography itself could detect @entity0 up to 10 years earlier than the conventional golden methods such as mammography, in particular in the younger @entity1 . However, the accuracy of thermography is dependent on many factors such as the symmetry of the breasts' temperature and temperature stability. A @entity1 's body temperature is known to be stable in certain periods after menstruation and it was found that the accuracy of thermography in @entity1 whose thermal images are taken in a suitable period (5th - 12th and 21st day of menstruation) is higher (80%) than the total population of @entity1 (73%). The stability of the body temperature will depend on physiological state. This paper examines the use of ANN to complement the infrared heat radiating from the surface of the body with other physiological data. Four backpropagation @entity1274 were developed and trained using the results from the Singapore General Hospital @entity1 ' physiological data and thermographs. Owing to the inaccuracies found in thermography and the low population size gathered for this project, the networks developed could only accurately diagnose about 61.54% of the @entity0 cases. Nevertheless, the basic neural network framework has been established and it has great potential for future development of an intelligent @entity0 diagnosis system. This would be especially useful to the teenagers and young adults who are unsuitable for mammography at a young age. An intelligent breast thermography-neural network will be able to give an accurate diagnosis of @entity0 and can make a positive impact on @entity1558 .
