International Journal of Fuzzy Systems

, Volume 20, Issue 4, pp 1385–1402 | Cite as

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

  • U. Rajendra AcharyaEmail author
  • 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


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.


Ovarian cancer Releif-F Data complexity k-NN Fuzzy forest Random forest 

Supplementary material

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Supplementary material 1 (PDF 6367 kb)


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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • U. Rajendra Acharya
    • 1
    • 2
    • 3
    Email author
  • Ayesha Akter
    • 4
  • Pradeep Chowriappa
    • 4
  • Sumeet Dua
    • 4
  • U. Raghavendra
    • 5
  • Joel E. W. Koh
    • 1
  • Jen Hong Tan
    • 1
  • Sook Sam Leong
    • 6
  • Anushya Vijayananthan
    • 6
  • Yuki Hagiwara
    • 1
  • Marlina Tanty Ramli
    • 8
  • Kwan Hoong Ng
    • 7
  1. 1.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  2. 2.Department of Biomedical Engineering, School of Science and TechnologySUSS UniversitySingaporeSingapore
  3. 3.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  4. 4.Computer ScienceLouisiana Tech UniversityRustonUSA
  5. 5.Department of Instrumentation and Control Engineering, Manipal Institute of TechnologyManipal UniversityManipalIndia
  6. 6.Department of Biomedical Imaging, Faculty of MedicineUniversity of MalayaKuala LumpurMalaysia
  7. 7.University of Malaya Research Imaging Centre, Faculty of MedicineUniversity of MalayaKuala LumpurMalaysia
  8. 8.Medical Imaging Unit, Faculty of MedicineUiTMSungai BulohMalaysia

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