Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework

Acharya, Rajendra U and Akter, Ayesha and Chowriappa, Pradeep and Raghavendra, U (2018) Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework. International Journal of Fuzzy Systems, 20 (4). pp. 1385-1402. ISSN 1562-2479

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Abstract

Ovarian cancer is one of the prime causes of mortality in women. Diagnosis of ovarian cancer using ultrasonography is tedious as ovarian tumors exhibit minute clinical and structural differences between the suspicious and non-suspicious classes. Early prediction of ovarian cancer will reduce its growth rate and may save many lives. Computer-aided diagnosis (CAD) is a noninvasive method for finding ovarian cancer in its early stage which can avoid patient anxiety and unnecessary biopsy. This study investigates the efficacy of a novel CAD tool to characterize suspicious ovarian cancer using Radon-transformed nonlinear features. The obtained dimension of the extracted features is reduced using Relief-F feature selection method. In this study, we have employed the fuzzy forest-based ensemble classifier in contrast to the known crisp rule-based classifiers.

Item Type: Article
Subjects: Engineering > MIT Manipal > Physics
Depositing User: MIT Library
Date Deposited: 23 Jun 2018 10:39
Last Modified: 23 Jun 2018 10:39
URI: http://eprints.manipal.edu/id/eprint/151335

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