Paper Info
Title
Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network
Abstract
The segregation among benign and malignant nasopharyngeal carcinoma (NPC) from endoscopic images is one of the most challenging issues in cancer diagnosis because of the many conceivable shapes, regions, and image intensities, hence, a proper scientific technique is required to extract the features of cancerous NPC tumors. In the present research, a neural network-based automated discrimination system was implemented for the identification of malignant NPC tumors. In the proposed technique, five different types of qualities, such as local binary pattern, gray-level co-occurrence matrix, histogram of oriented gradients, fractal dimension, and entropy, were first determined from the endoscopic images of NPC tumors and then the following steps were executed: (1) an enhanced adaptive approach was employed as the post-processing method for the classification of NPC tumors, (2) an assessment foundation was created for the automated identification of malignant NPC tumors, (3) the benign and cancerous cases were discriminated by using region growing method and artificial neural network (ANN) approach, and (4) the efficiency of the outcomes was evaluated by comparing the results of ANN. In addition, it was found that texture features had significant effects on isolating benign tumors from malignant cases. It can be concluded that in our proposed method texture features acted as a pointer as well as a help instrument to diagnose the malignant NPC tumors. In order to examine the accuracy of our proposed approach, 159 abnormal and 222 normal cases endoscopic images were acquired from 249 patients, and the classifier yielded 95.66% precision, 95.43% sensitivity, and 95.78% specificity.
Year
DOI
Venue
2020
10.1007/s11227-018-2587-z
The Journal of Supercomputing
Keywords
Field
DocType
Nasopharyngeal carcinoma, Nasopharyngeal carcinoma discrimination, NPC classification, Feature extraction, Texture feature, Artificial neural networks, Endoscopy images
Pattern recognition,Computer science,Local binary patterns,Parallel computing,Feature extraction,Histogram of oriented gradients,Region growing,Artificial intelligence,Classifier (linguistics),Artificial neural network,Nasopharyngeal carcinoma,Cancer
Journal
Volume
Issue
ISSN
76
2
1573-0484
Citations 
PageRank 
References 
18
1.03
17
Authors
7
Name
Order
Citations
PageRank
Mazin Abed Mohammed1835.83
Mohd Khanapi Abd Ghani220712.39
N. ArunKumar322915.60
raed41559.78
Salama A. Mostafa516621.72
Mohamad Khir Abdullah6824.45
M. A. Burhanuddin7482.84