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

Acharya, U. Rajendra and Akter, Ayesha and Chowriappa, Pradeep and Dua, Sumeet and Raghavendra, U. and Koh, Joel E. W. and Tan, Jen Hong and Leong, Sook Sam and Vijayananthan, Anushya and Hagiwara, Yuki and Ramli, Marlina Tanty and Ng, Kwan Hoong (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

Full text not available from this repository.
Official URL: https://doi.org/10.1007/s40815-018-0456-9

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. The proposed method is evaluated using 469 (non-suspicious: 238, suspicious: 231) subjects and achieved a maximum 80.60 ± 0.5% accuracy, 81.40% sensitivity, 76.30% specificity with fuzzy forest, an ensemble fuzzy classifier using thirty-nine features. The proposed method is robust and reproducible as it uses maximum number subjects (469) as compared to state-of-the-art techniques. Hence, it can be used as an assisting tool by gynecologists during their routine screening.

Item Type: Article
Uncontrolled Keywords: Data complexity; Fuzzy forest; k-NN; Ovarian cancer; Random forest; Releif-F
Subjects: R Medicine
Divisions: Faculty of Engineering
Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Oct 2019 06:54
Last Modified: 08 Oct 2019 06:54
URI: http://eprints.um.edu.my/id/eprint/22709

Actions (login required)

View Item View Item