Ovarian cancer stage based detection on convolutional neural network
Beant Kaur, Kulvinder Singh Mann, Manpreet Kaur Grewal · 2017
Ovarian cancer is the fifth most common cancer affecting women today. Ovarian cancer is a cancer that begins in the ovaries. The ovaries are female generative organs situated in the pelvis, approximately the size of an almond. The ovaries produce eggs (ova) for reproduction. In this research paper, Detect the ovarian cancer and found the stage of the cancer in the malignant cancer image. The proposed algorithm is used to feature extraction technique using SIFT algorithm. Any object there are many features, interesting points on the object, that can be extracted to provide and feature, a description of the object. In genetic algorithm used to optimize the extracted feature with the help of the fitness function. In fitness function depends upon three parameters i.e, each feature, total features and classification error rate. The detection of the ovarian cancer and stages found using a convolutional neural network. The accuracy is achieved with CNN classifier is 98.8% and with SVM is 85.01%. The performance parameters used are Sensitivity Specificity and accuracy.