Cervical cancer diagnosis using convolution neural network with conditional random field

Abstract

Cervical cancer is the second most common disease in women worldwide, and the Pap smear is one of the most used methods for detecting cervical cancer early on. Developing nations, such as India, must confront hurdles in order to manage an increasing number of patients on a daily basis. Various online and offline machine learning techniques were used on benchmarked data sets to diagnose cervical cancer in this paper. the importance of machine learning can be seen in the various fields as it provides various benefits in the completion of the task. Medical image analysis is done for diagnostic purposes in the medical form but creating pictures of the structures and activities inside the body. The use of machine learning for medical image analysis provides various benefits during the diagnosis of a person's diseases. CNN-CRF provides various applications for analyzing the structure and capturing the picture of the inside body structure of the human. Different applications of machine learning help in analyzing the different types of the medical image such as neural networks and CT scans. Medical image analysis is the area that has been largely benefited by machine learning.