Ovarian Cancer Detection Using K-Svm Algorithm - Sidhant, A; Sehgal, L (International Journal of Computer Sciences and Engineering, 2018)
Ovarian Cancer represents the main challenge among the gynecologic malignancies and early stage detection is of primary significance, because recently more than 2-3 of the patients present with development infection. Ovarian Cancer disease and treatment has measureable belongings on the superiority of patients of life with OC (ovarian cancer). In this study reviews existing related on eminence of life in users with OC to establish the significance of the topic. The main issues in the detecting process areas are the cancer detection on ultra sound image is not easy to identify on the foundation of gathering or image segmentation and the research work accuracy rate is 90 percent to 95 percent of Normal SVM existing systems. It can be refitted. The quality of the scan in ultrasound images are not appropriate for the system because the view of images is difficult to classify in terms of various segments or data clusters. In research work, implement Otsu technique is reliable and efficient method, used world-widely. It’s an all-around limiting strategy with dark estimation of picture. Otsu technique is a simplified, reliable and efficient method, used world-widely. It’s an all-around limiting strategy with dark estimation of picture. The classification and clustering is used k-SVM to train the cancer images in the each stage dataset and test the cancer detection and enhance the quality of the cancer image (MRI images). To compute the metric of performance like Accuracy Rate, Specificity and Sensitivity and compared with prior approaches i.e. accuracy and other performance metrics.