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Journal of Medical Signals and Sensors، جلد ۱۱، شماره ۲، صفحات ۶۷-۷۸
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Cervical Cancer Prediction by Merging Features of Different Colposcopic Images and Using Ensemble Classifier |
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| چکیده انگلیسی مقاله |
Background: Cervical cancer is a significant cause of cancer mortality in women, particularly in low-income countries. In regular cervical screening methods, such as colposcopy, an image is taken from the cervix of a patient. The particular image can be used by computer-aided diagnosis (CAD) systems that are trained using artificial intelligence algorithms to predict the possibility of cervical cancer. Artificial intelligence models had been highlighted in a number of cervical cancer studies. However, there are a limited number of studies that investigate the simultaneous use of three colposcopic screening modalities including Greenlight, Hinselmann, and Schiller. Methods: We propose a cervical cancer predictor model which incorporates the result of different classification algorithms and ensemble classifiers. Our approach merges features of different colposcopic images of a patient. The feature vector of each image includes semantic medical features, subjective judgments, and a consensus. The class label of each sample is calculated using an aggregation function on expert judgments and consensuses. Results: We investigated different aggregation strategies to find the best formula for aggregation function and then we evaluated our method using the quality assessment of digital colposcopies dataset, and our approach performance with 96% of sensitivity and 94% of specificity values yields a significant improvement in the field. Conclusion: Our model can be used as a supportive clinical decision-making strategy by giving more reliable information to the clinical decision makers. Our proposed model also is more applicable in cervical cancer CAD systems compared to the available methods. |
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| کلیدواژه‌های انگلیسی مقاله |
Aggregation strategy, artificial intelligence, cervical cancer, ensemble classifier, machine learning |
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| نویسندگان مقاله |
| Elham Nikookar
Department of Computer Engineering, University of Applied Science and Technology, Ahvaz, Iran
| Ebrahim Naderi
Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington D.C., United States
| Ali Rahnavard
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| نشانی اینترنتی |
http://jmss.mui.ac.ir/index.php/jmss/article/view/569 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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| نوع مقاله منتشر شده |
Original Articles |
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