Cervical Cancer Prediction by Merging Features of Different Colposcopic Images and Using Ensemble Classifier
عنوان مقاله: Cervical Cancer Prediction by Merging Features of Different Colposcopic Images and Using Ensemble Classifier
شناسه ملی مقاله: JR_JMSI-11-2_001
منتشر شده در در سال 1400
شناسه ملی مقاله: JR_JMSI-11-2_001
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:
Elham Nikookar - Department of Computer Engineering, Faculty of Engineering, Shiahd Chamran University of Ahvaz
Ebrahim Naderi - Department of Computer Engineering, University of Applied Science and Technology, Ahvaz, Iran
Ali Rahnavard - Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington D.C., United States
خلاصه مقاله:
Elham Nikookar - Department of Computer Engineering, Faculty of Engineering, Shiahd Chamran University of Ahvaz
Ebrahim Naderi - Department of Computer Engineering, University of Applied Science and Technology, Ahvaz, Iran
Ali Rahnavard - Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington D.C., United States
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 ۹۶% of sensitivity and ۹۴% 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.
کلمات کلیدی: Aggregation strategy, artificial intelligence, cervical cancer, ensemble classifier, machine learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1700123/