IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Machine Learning for Prediction of Lung Cancer

Machine Learning for Prediction of Lung Cancer
View Sample PDF
Author(s): Nikita Banerjee (College of Engineering and Technology, Rourkela, India) and Subhalaxmi Das (College of Engineering and Technology, Bhubaneswar, India)
Copyright: 2021
Pages: 26
Source title: Deep Learning Applications in Medical Imaging
Source Author(s)/Editor(s): Sanjay Saxena (International Institute of Information Technology, India) and Sudip Paul (North-Eastern Hill University, India)
DOI: 10.4018/978-1-7998-5071-7.ch005

Purchase

View Machine Learning for Prediction of Lung Cancer on the publisher's website for pricing and purchasing information.

Abstract

This work is focused on lung cancer prediction using machine learning technique. Lung cancer is one of the widespread diseases due to the growth of irregular cell in both the lungs as a result of which this irregular cell starts growing into tumour, and this tumour can be cancerous as well as non-cancerous. In the traditional approach CT scan images has been used based on the report image segmentation has been done to remove the noise so that a clear picture can be generated to detect the location of tumor. Once the location is known then classification or clustering approach can be used to predict the stage of cancer. Previously supervised machine learning algorithm has been used to predict lung cancer. In this work a prediction model is proposed that is based on the median filter, watershed segmentation, and then feature extraction has done like texture and region. And on the extracted feature classification technique was applied for prediction of cancer.

Related Content

Devika G., Asha G. Karegowda. © 2021. 39 pages.
Kanchan Sarkar, Bohang Li. © 2021. 38 pages.
Sumesh Sasidharan, M. Yousuf Salmasi, Selene Pirola, Omar A. Jarral. © 2021. 22 pages.
Hmidi Alaeddine, Malek Jihene. © 2021. 14 pages.
Nikita Banerjee, Subhalaxmi Das. © 2021. 26 pages.
Amiya Kumar Dash, Puspanjali Mohapatra. © 2021. 16 pages.
Janani Viswanathan, N. Saranya, Abinaya Inbamani. © 2021. 22 pages.
Body Bottom