New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling

Abstract

Abstract The progress in digital histopathology for computer-aided diagnosis leads to advancement in automated histopathological image classification system. However, heterogeneity and complexity in structural background make it a challenging process. Therefore, this paper introduces robust and reliable new bag-of-feature framework. The optimal visual words are obtained by applying proposed reinforcement cat swarm optimization algorithm. Moreover, the frequency of occurrence of each visual words is depicted through histogram using new weighted Gaussian mixture modelling method. Reinforcement cat swarm optimization algorithm is evaluated on the IEEE CEC 2017 benchmark function problems and compared with other state-of-the-art algorithms. Moreover, statistical test analysis is done on acquired mean and the best fitness values from benchmark functions. The proposed classification model effectively identifies and classifies the different categories of histopathological images. Furthermore, the comparative experimental result analysis of proposed reinforcement cat swarm optimization-based bag-of-feature is performed on standard quality metrics measures. The observation states that reinforcement cat swarm optimization-based bag-of-feature outperforms the other methods and provides promising results.