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dc.contributor.authorPutri, Erlinda
dc.date.accessioned2022-12-27T06:27:30Z
dc.date.available2022-12-27T06:27:30Z
dc.date.issued2023-03-01
dc.identifier.urihttp://repository.unmul.ac.id/handle/123456789/42872
dc.description.abstractCervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy.en_US
dc.publisherInternational Journal of Artificial Intelligenceen_US
dc.subjectArtificial neural networken_US
dc.subjectCervical canceren_US
dc.subjectComputed tomographyen_US
dc.subjectGray level co-occurrence matrixen_US
dc.subjectSnake modelen_US
dc.titleArtificial neural network for cervical abnormalities detection on computed tomography imagesen_US


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