Nosema Pathogenic Agent Recognition Based on Geometrical and Texture Features Using Neural Network Classifier

Abstract

This paper is concerned with the combination of microscopic image processing tools and artificial intelligence in order to detect and recognize the Nosema disease, which affects honey bees. In fact, the use of imaging processing tools in medical and biological sciences was significantly increased, especially in the detection and diagnosis of diseases. Our work focuses on a segmentation method which analyses the objects of an image and decides if it is Nosema or not. A set of the significant values of geometric and texture features has been calculated and fused as a definition for a Nosema cell. A MultiLayer neural network was applied as classifier to detect and recognize this disease on Nosema images. Finally, our automatic segmentation approach shows a 91% of accuracy, in the process of identifying the Nosema disease.