Journal
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021)
Volume -, Issue -, Pages 827-835Publisher
IEEE
DOI: 10.1109/ICICT50816.2021.9358570
Keywords
Pap Smear; Support Vector Machines (SVMs); Active Contours; Segmentation; Bilateral filter; Local Gaussian Fitting Energy
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Cervical cancer is a common disease in females, but early detection, screening, and precautions can lead to higher recovery rates. The pap-smear test is preferred, and machine learning methods can assist in accurately classifying cancer cells.
Cervical cancer is one of the diseases which are most prevalent in females. This is seen when some changes occur in a woman's cervix. To further complicate the issue these cancer cells can spread to other organs such as the liver, bladder, rectum, and even lungs. Earlier detection, screening, and careful precautions have seen higher rates of recovery. Colposcopy and pap-test are two methods to obtain the cells and study the subject. The pap-smear test is more preferred over Colposcopy as it's pain-free, low-cost, and has a more distant range. A proper cell image acquisition and accurate segmentation are the key steps for the correct classification of cells. This article presents a Machine Learning (ML) classification method applied to the Herlev pap-smear image Dataset, using Support Vector Machines (SVMs). For the segmentation phase, active contour models that are driven using Gaussian Fitting Energy were utilized. These segmented images were compared with the manually annotated images that were provided by professional cytologists. The dice index was the parameter used from the comparison, which reported a match of 92% With polynomial SVMs highest classification accuracy was 95%.
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