Cervical Cancer Segmentation using Fuzzy Support Vector Machine Algorithm - P.A., Gowri Sankar; R., Aparna; K., Sathishsharama; K., Aravinth; V., Thirumalai; M., Sathishkumar (Journal of Soft Computing Paradigm, 2024)
Cervical cancer is a dangerous disease, particularly prevalent in developing countries where public awareness is low. The Papanicolaou test, commonly known as the Pap test, is the most widely used method to detect cervical cancer, which develops in the cervix and affects many women. Image processing algorithms play an important role in the segmentation of the cancerous region in cervical images. The fuzzy support vector machine (FSVM) algorithm is used to segment the cancerous regions in cervical cancer images. This method effectively separates the cervical cancer regions from the background in these images. The K-means classification algorithm is another existing method applied to cervical cancer images. The results of the existing and proposed segmentation algorithms are compared using quality measurement techniques such as accuracy and precision. The proposed FSVM algorithm demonstrated the highest accuracy (98%) compared to the previous algorithms.
Keywords: Cervical Cancer; Image Segmentation; FSVM; Image Quality; k means