Execution Analysis of Clarity Locale Segmentation for Condition Recognition Utilizing Genetic Algorithm Method

Abstract

The collection of fluid at the back of the fetal neck, known as nuchal translucency (NT), is linked to chromosomal abnormalities and early heart failure in the first trimester of pregnancy. Using the Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification algorithm, this research presents an effective way for recognising and localising the NT region in fetus images in which noise removed. Then, pattern features are extracted Initially, the noises in fetus images are detected and eliminated using directional filtering technique and then Gabor transform from the magnitude of Gabor transformed fetus image and then they are optimized using Genetic Algorithm (GA) approach. The extracted GLCM, ELBP and LTP features are integrated into feature vector for further classifications. The size of constructed feature vector is high and leads to high computation time for the classification process. These optimized feature set is classified using CANFIS. Finally, the graph cut segmentation method is used for segmenting the NT region. This proposed method is practically used in many health care centers in rural areas.