Non-small-cell lung cancer prediction using radiomic features and machine learning methods

Abstract

One of the primary causes of deaths related to cancer all over the world is Lung cancer. The history of the patient and his histological classification in terms of lung cancer has provided critical information regarding the characteristics of tissues and anatomical locations. There are many different studies that have depicted the radiomic features and their power of prediction in the detection of lung cancer. But its quantitative size in terms of data is large and has been resulting in major challenges in the algorithms of classification. In order to overcome this, symbolic approach to data analysis which employs many different quantitative data is proposed. The work further investigates different techniques of feature selection in order to predict the histologic subtypes of lung cancer by using either symbolic data or the radiomic features. These features have been extracted by using a gray-level co-occurrence matrix (GLCM), the Gabor filter and the fusion that was achieved by making use of concatenation once there is a normalization of the Z score. The results of the experiment have proved that the proposed method had a better performance compared to the other methods.

KEYWORDS:

• Lung cancer
• non-small-cell lung cancer (NSCLA)
• gray-level co-occurrence matrix (GLCM)
• minimum redundancy maximum relevance (MRMR)
• correlation-based feature selection (CFS)
• Naïve Bayes and decision tree

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

S. Shanthi

S. Shanthi born in Pollachi, Tamilnadu, India in 1982. She received B.E degree in Computer science and Engineering from Bharathiar University, Coimbatore and M.E. degree in Computer Science and Engineering from Anna University, Chennai. She has published papers in International Journal and Conferences. She is a member of an ISTE. She is an Assistant Professor in Department of Computer Science and Engineering at Sri Eshwar College of Engineering, Coimbatore. Her research interests include Data Mining, Machine Learning, Image Processing and Symbolic Data Analysis.

N. Rajkumar

Dr N. Rajkumar obtained his Bachelor's Degree in Computer Science and Engineering from Madurai Kamaraj University in 1991 and His Masters in Computer Science and Engineering in 1995 from Jadavpur University, Kolkatta. He has completed his Masters in Business Administration in Human Resource Management from IGNOU in the year 2003. His doctorate is in the field of Data Mining, which he completed in 2005 from Bharathiar University. He has been awarded competitive Research Grants from various National Government Funding Agencies including AICTE, VSSC, DRDO, CSIR, ISRO. He is a Life Member of Indian Society for Technical Education, Life Member of Computer Society of India (CSI) and Fellow of the Institution of Engineers (FIE). He served as Reviewer in IEEE Systems Journal, International Journal of Computers and Electrical Engineering, Elsevier, International Journal of Computers and Applications, ACTA Press and Editorial Board Member in Journal of Engineering Students, Hyderabad, India He is a Lead Auditor (ISO 9000:2000) and Consultant at several Academic Institutions and also responsible for mentoring new Engineers and Contributors, delivering Technical Presentations.