• S.K.RAJALAKSHMI
• Dr.S.SIVAGAMASUNDARI

Early diagnosis of fetal abnormalities can be performed using proper screening procedure. The screening may sometime look as an exhaustive one for medical experts. Hence, automation with manual inspection provides better assistance to endoscopes in finding out the abnormal fetal images. In this paper, we study an automated classification of fetal abnormalities during first trimester pregnancy period using Artificial Bee Colony (ABC) and ANFIS. Initially, the image is pre-processed to remove the noise and other complexion present in raw image dataset. In the second stage, an ABC algorithm is used to segment the image into labels based on region based segmentation. In the final stage, the image labels are classified using ANFIS classifier, which uses labels from the previous stage as its input.This automated classification model classifies the stage of abnormality using ground truth value. The proposed classification model is tested with large fetal test image datasets and it is compared with existing algorithms to prove its effectiveness in detecting the fetal abnormalities.

ANFIS classifier, ABC, Fetal Abnormalities, segmentation, classification

"ANALYSIS OF DIFFERENT CLASSIFIERS FOR DETECTION OF FETAL ABNORMALITIES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.396-401, January-2019, Available :http://www.jetir.org/papers/JETIR1901446.pdf

"ANALYSIS OF DIFFERENT CLASSIFIERS FOR DETECTION OF FETAL ABNORMALITIES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp396-401, January-2019, Available at : http://www.jetir.org/papers/JETIR1901446.pdf