Decision-Level Fusion Scheme For Nasopharyngeal Carcinoma Identification Using Machine Learning Techniques

Mohammed, Mazin Abed and Mohd Khanapi, Abd Ghani and M.A., Burhanuddin and Arunkumar, N. and Mostafa, Salama A. and Ibrahim, Dheyaa Ahmed and Abdullah, Mohamad Khir and Jaber, Mustafa Musa and Abdulhay, Enas and Ramirez-Gonzalez, Gustavo (2020) Decision-Level Fusion Scheme For Nasopharyngeal Carcinoma Identification Using Machine Learning Techniques. Neural Computing and Applications, 32 (3). 625 - 638. ISSN 0941-0643

Text DECISION-LEVEL FUSION NPC PAPER.PDF Restricted to Registered users only Download (1MB)

Abstract

Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple scorelevel fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decisionbased fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%.

Item Type:	Article
Uncontrolled Keywords:	Nasopharyngeal Carcinoma, Machine Learning Techniques, Feature-Based Decision-Level Fusion, Endoscopic Images, Local Binary Patterns, Support Vector Machines, K -Nearest Neighbors’ Algorithm, Artificial Neural Network
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	27 Apr 2021 13:00
Last Modified:	27 Apr 2021 13:00
URI:	http://eprints.utem.edu.my/id/eprint/25020
Statistic Details:	View Download Statistic

Actions (login required)

View Item