AUTOMATIC DETECTION OF FUNGI IN MICROSCOPIC LEUCORRHEA IMAGES BASED ON CONVOLUTIONAL NEURAL NETWORK AND MORPHOLOGICAL METHOD

Abstract

Leucorrhea routine test is one of the most widely used tests in gynecological examinations, and fungi inspection is vital for gynecological test because fungi is an important evidence for fungal vaginitis. In order to improve detection accuracy, an automatic identification of fungi in microscopic leucorrhea images based on convolutional neural network (CNN) and morphological method is proposed in this paper. First, we use the maximum inter-class variance method to segment original image and obtain possible fungi subimages. Then, a fully trained CNN is applied to recognize fungi. Finally, morphological method, such as template match method and concave point detection method, is used to further classify the selected candidate to improve recognize accuracy. In experiments, the method using CNN and morphological method achieved 93.26% accuracy.  

Existing System

Filtering methods, edge based detection method

Proposed System

In this paper, we propose an automatic identification of fungi in microscopic leucorrhea images based on convolutional neural network and morphological method. First, we use the maximum inter-class variance method to segment original image and obtain possible fungi subimages. Then, a fully trained CNN is applied to recognize fungi. Finally, an improved morphological method is used to further classify the selected candidate to improve recognize accuracy.  

Architecture

BLOCK DIAGRAM