A Novel Framework for Detection of Cervical Cancer
DOI:
https://doi.org/10.51983/ajeat-2018.7.2.1016Keywords:
Dual-Tree Discrete Wavelet Transform, Curvelet Transform, Contour Transform, K-Means clustering, Gray Level co-occurance matrixAbstract
Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.
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