Variants of Artificial Bee Colony algorithm and its applications in medical image processing

Abstract

The Artificial Bee Colony (ABC) technique is a highly effective method of optimization inspired by the behavior of bees. Notably, the importance of the ABC algorithm is increasing after artificial intelligence, and automatic decision-making techniques are popularized in almost every field. The analysis of images obtained from medical imaging devices attracts the attention of artificial intelligence researchers because of the importance of these images for human health. Although the ABC algorithm is very humid for medical image analysis, there is no comprehensive literature review of medical image analysis techniques. This study includes a comprehensive survey of academic studies including classification, enhancement, clustering, and segmentation of medical images using ABC. The academic studies between the years 2010–2020 are examined, and 95 studies are presented in total. 42 of these studies consist of medical image analysis studies. Of the selected studies, 20 studies are related to image classification, 15 studies are related to image enhancement, 18 academic studies are related to image clustering, and 42 studies are related to image segmentation methods. The findings of this study show that the ABC method for medical image analysis has positive effects on classification, segmentation, clustering, and enhancement methods, and the use of the ABC method has become more common. We hope that this study will help new researchers to use the ABC method.