Sakarya University Journal of Computer and Information Sciences

Yazarlar: Fatma Betül AKYOL, Oğuz ALTUN

Konular:Bilgisayar Bilimleri, Yapay Zeka

DOI:10.35377/saucis.03.02.722670

Anahtar Kelimeler:Cervical cancer,Pap-smear,Segmentation,Classification

Özet: Pap-smear test is used to detect cervical cancer, which ranks fourth in the ranking of cancer diseases in women worldwide. In this study, it is aimed to design a computer based decision system that can detect cervical cancer at an early stage. Normal and abnormal cells are found in the cervix images obtained as a result of the pap-smear test and the abnormal cells are marked on the image. The features extracted from the images were examined with pathologists and a dataset was created. For each of the 917 images in the Herlev dataset, these features were extracted and stored in a dataset. Support Vector Machines (SVM), Naive Bayes, Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K- Nearest Neighbor (KNN) methods were applied to the created dataset, and accuracy values between 83% and 92% were obtained.

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