Detection and segmentation of cancer regions in cervical images using fuzzy logic and adaptive neuro fuzzy inference system classification method

Abstract

AbstractCervical cancer is one of the most severe death causing cancers in developing countries. The mortality rates of the cervical cancer are high in developing countries due to their unawareness about such cancer. This article proposes an efficient Fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) classification method based cancer region detection and segmentation in cervical images. The thick and thin edges are detected using fuzzy logic and these detected edges are fused pixel level image fusion technique. In this article, fuzzy rules are constructed for detecting edges for cancer region detection. Then, Gabor transform is applied on the fused cervical image for the transformation of pixels. The texture features are extracted from the Gabor transformed image and these features are classified using ANFIS classification approach. Further, morphological operations are used to segment the cancer regions in classified abnormal cervical image. The average classification rate of the proposed cervical cancer detection system is about 98.8%. The proposed cervical cancer segmentation methodology stated in this article achieves 98.1% of sensitivity, 99.4% of specificity, and 99.3% of accuracy.