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

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1385-1402
Number of pages18
JournalInternational Journal of Fuzzy Systems
Volume20
Issue number4
DOIs
Publication statusPublished - 01-04-2018

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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    Acharya, U. R., Akter, A., Chowriappa, P., Dua, S., Raghavendra, U., Koh, J. E. W., Tan, J. H., Leong, S. S., Vijayananthan, A., Hagiwara, Y., Ramli, M. T., & Ng, K. H. (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), 1385-1402. https://doi.org/10.1007/s40815-018-0456-9