Trainable model for segmenting and identifying Nasopharyngeal carcinoma. (October 2018)

• Record Type: Journal Article
• Authors: Mohammed, Mazin Abed; Abd Ghani, Mohd Khanapi; Arunkumar, N.; Mostafa, Salama A.; Abdullah, Mohamad Khir; Burhanuddin, M.A.
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 372
• Page End: 387
• Publication Date: 2018-10
• Subjects: Nasopharyngeal carcinoma -- Trainable segmentation -- Feature extraction -- Texture feature -- Artificial neural networks -- Genetic algorithm -- Support vector machine -- Endoscopic images; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.044
• Languages: English
• ISSNs: 0045-7906

Abstract

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.