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

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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. 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.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    U. Rajendra Acharya, Joel E. W. Koh, Jen Hong Tan & Yuki Hagiwara

  2. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, 599491, Singapore

    U. Rajendra Acharya

  3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

  4. Computer Science, Louisiana Tech University, Ruston, LA, USA

    Ayesha Akter, Pradeep Chowriappa & Sumeet Dua

  5. Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal, 576104, India

    U. Raghavendra

  6. Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Sook Sam Leong & Anushya Vijayananthan

  7. University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Kwan Hoong Ng

  8. Medical Imaging Unit, Faculty of Medicine, UiTM, Sungai Buloh, Malaysia

    Marlina Tanty Ramli

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  1. U. Rajendra Acharya
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Correspondence to U. Rajendra Acharya.

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Acharya, U.R., Akter, A., Chowriappa, P. et al. Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework. Int. J. Fuzzy Syst. 20, 1385–1402 (2018). https://doi.org/10.1007/s40815-018-0456-9

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