An adaptive genetic algorithm as a supporting mechanism for microscopy image analysis in a cascade of convolution neural networks

The analysis of microscopy images allows for making the description of given samples containing different microscopic organisms. It is important due to the life phase analysis of these organisms. In this paper, an adaptive technique composed of a genetic algorithm (GA) and a cascade of the convolutional classifiers for image analysis is proposed. A GA is proposed for an indication of the likelihood of belonging to the appropriate class. The indicated probability is important due to the next step of measuring the results obtained from the cascade of neural classifiers. The main idea is a hybridization of these two techniques and generalization of the heuristic solution to seeking fitness function coefficients. The proposed solution is described and tested on two databases.In the case of binary classification, the obtained accuracy was 7.5% higher compared to the use of the classical approach like learning transfer. The proposed solution is discussed in relation to the analysis of individual components, other methods, and advantages/disadvantages.