Gitanjali Wadhwa, Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Mansi Mathur, Computer Science and Engineering, J.C. Bose University of Science and Technology, YMCA Faridabad, Haryana
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :471-476
Abstract
The important part of female reproductive system is ovaries. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The place of these ductless almond shaped tiny glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are several reasons due to which ovarian cancer can arise but it can be classified by using different number of techniques. Early prediction of ovarian cancer will decrease its progress rate and may possibly save countless lives. CAD systems (Computeraided diagnosis) is a noninvasive routine for finding ovarian cancer in its initial stages of cancer which can keep away patients’ anxiety and unnecessary biopsy. This review paper states us about how we can use different techniques to classify the ovarian cancer tumor. In this survey effort we have also deliberate about the comparison of different machine learning algorithms like KNearest Neighbor, Support Vector Machine and deep learning techniques used in classification process of ovarian cancer. Later comparing the different techniques for this type of cancer detection, it gives the impression that Deep Learning Technique has provided good results and come out with good accuracy and other performance metrics.
Keywords
Machine Learning, Deep Learning, Ovarian Cancer, CNN
Cite this article
Gitanjali Wadhwa and Mansi Mathur, “Detection of Ovarian Tumor Using Machine Learning Approaches: A Review”, Innovations in Information and Communication Technology, pp.471-476, December 2020.
Copyright
© 2020 Gitanjali Wadhwa and Mansi Mathur. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
