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 language | English |
|---|---|
| Pages (from-to) | 1385-1402 |
| Number of pages | 18 |
| Journal | International Journal of Fuzzy Systems |
| Volume | 20 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 01-04-2018 |
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All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence
Cite this
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Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework. / Acharya, U. Rajendra; Akter, Ayesha; Chowriappa, Pradeep; Dua, Sumeet; Raghavendra, U.; Koh, Joel E.W.; Tan, Jen Hong; Leong, Sook Sam; Vijayananthan, Anushya; Hagiwara, Yuki; Ramli, Marlina Tanty; Ng, Kwan Hoong.
In: International Journal of Fuzzy Systems, Vol. 20, No. 4, 01.04.2018, p. 1385-1402.Research output: Contribution to journal › Article
TY - JOUR
T1 - Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework
AU - Acharya, U. Rajendra
AU - Akter, Ayesha
AU - Chowriappa, Pradeep
AU - Dua, Sumeet
AU - Raghavendra, U.
AU - Koh, Joel E.W.
AU - Tan, Jen Hong
AU - Leong, Sook Sam
AU - Vijayananthan, Anushya
AU - Hagiwara, Yuki
AU - Ramli, Marlina Tanty
AU - Ng, Kwan Hoong
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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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U2 - 10.1007/s40815-018-0456-9
DO - 10.1007/s40815-018-0456-9
M3 - Article
AN - SCOPUS:85044289115
VL - 20
SP - 1385
EP - 1402
JO - International Journal of Fuzzy Systems
JF - International Journal of Fuzzy Systems
SN - 1562-2479
IS - 4
ER -