Soft computing for human spermatozoa morphology

A fuzzy data fusion approach was used for human sperm morphology recognition analysis using 3-CCD camera image data. Fuzzy operators were used to create the relationship between abnormal sperm morphology and normal sperm aberrations after digitizing the images. The approaches have focused on frequencies of sperm with either abnormal morphology in semen analysis samples. In order to establish whether various shapes of membership functions would behave in similar manners, the individual spermatozoon was also classified according to their head, tail, and neck shapes into symmetrical, asymmetrical, irregular and amorphous categories based on the fuzzy region. The images results demonstrated how these findings facilitated approaches on the 100 semen samples. The average probability of morphology recognition analysis was equal to 95% and the average probability of unknown parameter was equal to 4.5%. We showed in this paper that fuzzy fusion morphology provided a unified and consistent framework to express different shapes of human spermatozoa.