Neutrosophic graph cut-based segmentation scheme for efficient cervical cancer detection

Cervical cancer is the most serious category of that has very low survival rate in women’s community around globe. This probability women society affected by this cervical can be potentially enhanced if it detected at an early stage as they do not provide any realizable degree symptoms phase. needs to through periodical checkups. Hence, objective proposed work focuses on merits Neutrosophic Graph Cut-based Segmentation (NGCS) facilitated over pre-processed images. NGCS-based segmentation mainly employed for investigating overlapping contexts smear images better classification accuracy. responsible partitioning input preprocessed image into a diversified number non-overlapping regions aids perception convenience. In segmentation, transformed set and indeterminacy filter depending estimated value integrates intensity spatial information image. The utilized plays anchor role minimizing associated with each information. Then graph defined unique weights are assigned pixels based value. Finally, maximum flow approach applied determining optimal results. results detection technique proved excellent average 13% compared traditional cut oriented approaches.