Designing an Android-Based Burn Rate Pattern Detection Application Model.

In the medical area, the role of computer in pattern recognition of a disease is very much needed. It can be help for making treatment decision by first knowing the pattern of the disease. One of this model is the initial pattern recognition of burns that experienced by patients. Detecting the initial pattern of burn rates on the body will help the medical team immediately make decisions regarding patient burn level. To detect the initial pattern of burn rates, the appropriate method is to use the fisherface algorithm. This algorithm is used because of the ability to extract important information in imaging burn patterns on the body through the calculation of the average vector matrix and the covariance matrix in the pattern imaging database. In the process, the fisherface algorithm will generate an eigenface which is used for pattern recognition. Eigenface is the basis for calculating the burn patterns value on the body which represent the individual values for one or more pattern images of the burns on body. The using of fisherface and eigenface enable us to detect the degree of burns. The computational process will help in determining the degree of burns on body through facial recognition software. [ABSTRACT FROM AUTHOR]