Edge Enhancement and Detection Approach on Cervical Cytology Images

Cervical cancer is a prevalent and fatal disease that affects women all over the world. This roughly 0.5 million annually kills 0.3 people. Recently, significant amount of literature has emerged around advancement technologies for identifying cervical cells in women. Previously, diagnosing was done manually, which could lead to false positives or negatives. The best way interpreting Pap smear images automatically diagnose are still up debate among researchers. Method used this study contrast enhancement technique pre-processing edge detection-based segmentation nucleus. In study, average performance results method showed an accuracy 96.99% seven-class problem using Herlev dataset. present finding also support concluded achieved algorithm nucleus detection improved by 6.15% when comparing previous work. value lines earlier approach above 90% seven class cells. major feature suggested improvement ability anticipate aberrant normal. Adding more classifiers improve system even further. Therefore, screening might utilize framework identify who have precancerous lesions.