An Ensemble Approach to Precancerous Cervical Lesion Classi cation from Cervigram Images

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  • <b>An Ensemble Approach to Precancerous Cervical Lesion Classification from Cervigram Images</b>

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<p>In this study, we propose a semi-automated system for detecting cervical cancer from cervigram photographs of affected cervix regions. Cervical cancer is among the most common cancers affecting women in the world particularly in developing countries where few population have access to proper screening due to high costs of laboratory testing. For this reason a simple inexpensive test by visual inspection with acetic acid (VIA) is used where the cervix region is observed with the naked eye for change in color, texture and appearance. We consider that applying adequate image processing techniques to the captured images during VIA is effective to assist gynecologist (doctor) for detecting, diagnosing and examining the cervix region based on the visual inspection observations. That is, it is possible to construct a kind of computer aided systems for detecting and diagnosing cervical cancers. In our framework we first segment an input image into lesions of interest by GrabCut algorithm, and next extract many color- and texture-based features by using image processing.Then based on these extracted features the segmented image is categorized as cancerous “malignant” or non-cancerous “benign” by using ensemble classification methods combined with 3 or 5 machine learning algorithms. We conducted some experiments using real cervigram images and found through the statistical analysis that only 10–13 extracted features can be sufficient to detect cervix cancer and our method comparatively improved the detection accuracy compared to visual eye inspection.</p>

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