Histopathological Image Classification Using Efficient Bag-of-Features and Whale Optimization Algorithm

Abstract

An automated digital histopathological image analysis is a complex procedure because of the accessibility of different morphological structures in the tissue images. For the equivalent, an efficient bag-of-features strategy has been proposed for histopathological image classification. Taken care bag-of- features method, the visual vocabulary is created by K-implies technique which indicates biasedness towards the at first chosen cluster heads. This paper proposed a whale optimization algorithm that utilizes bag of-features method. The proficiency of the proposed efficient bag-of- features whale optimization method has been tried on different standard benchmark functions. By combining both efficient bag-of-features based classification with whale optimization algorithm has been analyzed on blue histology image dataset and compared with different other meta-heuristic based bag-of-features methods. The explanatory result outcomes justify that the proposed technique outperforms the best in state-of-the-art strategy.