This paper is published in Volume-4, Issue-6, 2018
Area
Artificial Intelligence
Author
Uroosa Shafi, Sugandha Sharma
Org/Univ
Chandigarh University, Ajitgarh, Punjab, India
Pub. Date
23 November, 2018
Paper ID
V4I6-1254
Publisher
Keywords
CAD (Computer Aided Diagnosis), CNN (Convolutional Neural Network), MLP (Multi-Layer Perceptron) and SVM (Support Vector Machine)

Citationsacebook

IEEE
Uroosa Shafi, Sugandha Sharma. Ovarian cancer sign, symptoms and detection techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Uroosa Shafi, Sugandha Sharma (2018). Ovarian cancer sign, symptoms and detection techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 4(6) www.IJARIIT.com.

MLA
Uroosa Shafi, Sugandha Sharma. "Ovarian cancer sign, symptoms and detection techniques." International Journal of Advance Research, Ideas and Innovations in Technology 4.6 (2018). www.IJARIIT.com.

Abstract

The current medical field is improved in many ways by accessing the applications of the technology. Digital image processing is one of them that being fascinating for researchers as well as for doctors such as ultrasound images and others. Due to various reasons, the diseases are growing rapidly, Cancer is one of them. Basically, cancer is a disease in which the blood cells grow uncontrollably and abnormal that causes diseases. Ovarian cancer is the most occurred form of the disease in females and every year the majority of females are survived from it. The cancer is produced in the ovaries and spread in the other parts of the body. The detection and diagnosis are crucial in the early stages because of the diagnosis become harder at the last stages. In the research, a deep representation of ovarian cancer is described as its generating process, signs and symptoms and the major causes of ovarian cancer. There are also descriptions of various diagnosis techniques that helped to discover the cancer cells and treatment of the patient.