Detection of Ovarian Cancer using Deep Learning: A Survey

Abstract

The term "ovarian cancer" (OC) refers to the most frequent type of gynecological cancer. The female reproductive system would not function properly without the ovary. Their significance is enhanced by the fact that these small glands produce female sex hormones as well as female gametes. These almond-shaped glandular organs sit directly on either side of the uterus and are connected to it via the ovarian ligament; they do not contain any tubes. There are many potential causes of ovarian cancer, but fortunately there are also many potential methods of diagnosis; one of these is the convolutional neural network. This article summarizes how convolutional neural networks could be used to classify ovarian cancer tumors and what other treatments are out there for the disease. some machine learning algorithms are used in ovarian cancer detection, and throughout the course of this research work, we compared some of them. These algorithms include K-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. Following a comprehensive analysis of available methods, the Deep Learning Technique was shown to be the most productive in its detection of ovarian cancer.