A predictive function optimization algorithm for multi-spectral skin lesion assessment

The newly introduced Kubelka-Munk Genetic Algorithm (KMGA) is a promising technique used in the assessment of skin lesions. Unfortunately, this method is computationally expensive due to its function inverting process. In the work of this paper, we design a Predictive Function Optimization Algorithm in order to improve the efficiency of KMGA by speeding up its convergence rate. Using this approach, a High-Convergence-Rate KMGA (HCR-KMGA) is implemented onto multi-core processors and FPGA devices respectively. Furthermore, the implementations are optimized using parallel computing techniques. Intensive experiments demonstrate that HCR-KMGA can effectively accelerate KMGA method, while improving its assessment accuracy as well.