Trainable model for segmenting and identifying Nasopharyngeal carcinoma

Mohammed, Mazin Abed and Abd Ghani, Mohd Khanapi and Arunkumar, N. and A. Mostafa, Salama and Abdullah, Mohamad Khir and Burhanuddina, M.A. (2018) Trainable model for segmenting and identifying Nasopharyngeal carcinoma. Computers and Electrical Engineering, 71 (4). pp. 372-387. ISSN 0045-7906

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Abstract

Nasopharyngeal carcinoma (NPC) is a multifaceted cancer tumor that makes its diagnosis challenging. NPC has a consistently diffusive enlargement that makes its resection exceptionally challenging. The pathological identification of NPC and comparing typical and anomalous tissues require a set of advanced strategies for the extraction of features. The use of medical images to diagnoses NPC tumor depends on tumor shape, region, and intensity. This paper proposes a novel approach for diagnosing NPC from endoscopic images. The approach includes a trainable segmentation for identifying NPC tissues, genetic algorithm for selecting the best features, and support vector machine for classifying NPC. The proposed approach is validated by comparing the number of classified NPC cases against the manual approach of ENT specialists. The approach shows a high precision of 95.15%, sensitivity of 94.80%, and specificity of 95.20%. Additionally, the optimized feature selection provides straightforward and efficient classification results.

Item Type:	Article
Uncontrolled Keywords:	Nasopharyngeal carcinoma; Trainable segmentation; Feature extraction; Texture feature; Artificial neural networks; Genetic algorithm; Support vector machine; Endoscopic images
Subjects:	R Medicine > RC Internal medicine T Technology > T Technology (General) T Technology > T Technology (General) > T11.95-12.5 Industrial directories > T58.6-58.62 Management information systems
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	UiTM Student Praktikal
Date Deposited:	06 Jan 2022 02:33
Last Modified:	06 Jan 2022 02:33
URI:	http://eprints.uthm.edu.my/id/eprint/5142

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