Analysis of cancer in histological images: employing an approach based on genetic algorithm - Taino, Daniela F. [UNESP]; Ribeiro, Matheus G. [UNESP]; Roberto, Guilherme F.; Zafalon, Geraldo F. D. [UNESP]; do Nascimento, Marcelo Z.; Tosta, Thaína A. A.; Martins, Alessandro S.; Neves, Leandro A. [UNESP] (Springer Nature, 2021)
The analysis of histological images is based on visual assessment of tissues by specialists using an optical microscopy. This task can be time-consuming and challenging, mainly due to the complexity of the structures and diseases under investigation. These facts have motivated the development of computational methods to support specialists in research and decision-making. Despite the different computational strategies available in the literature, the solutions based on genetic algorithm have not been fully explored to provide the best combination of features, selection algorithms and classifiers. In this paper, we describe an approach based on genetic algorithm able to evaluate a significant number of features, selection methods and classifiers in order to provide an acceptable association for the diagnosis and pattern recognition of non-Hodgkin lymphomas and colorectal cancer. The chromosomal structure was represented with four genes. The evaluation and selection of individuals, as well as the crossover and mutation processes, were defined to distinguish the groups under investigation, with the highest AUC value and the smallest number of features. The tests were performed considering 1512 features from histological images, different population sizes and number of iterations. An initial population of 50 individuals and 50 iterations provided the best result (AUC value of 0.984) for the colorectal histological images. For non-Hodgkin lymphoma images, the best result (AUC value of 0.947) was obtained with a population of 500 individuals and 50 iterations. The proposed methodology with detailed information regarding the methods, features and best associations are relevant contributions for the community interested in the study of pattern recognition of colorectal cancer and lymphomas.
Keywords: Best association; Classification; Colorectal cancer; Feature selection; Genetic algorithm; Non-Hodgkin lymphoma