Detection of Cervix Cancer from Pap-smear Images

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.

Pap-Smear Görüntülerinden Rahim Ağzı Kanseri Tespiti

Dünya çapında kadınlarda görülen kanser hastalıkları sıralamasında dördüncü sırada yer alan rahim ağzı kanserini erken aşamada tespit edebilmek için pap-smear testi kullanılmaktadır. Bu çalışma kapsamında rahim ağzı kanserini erken aşamada tespit edebilecek bilgisayar temelli bir karar sisteminin tasarlanması amaçlanmıştır. Pap- smear testi sonucunda elde edilmiş olan serviks görüntülerinde normal ve anormal özellikli hücreler bulunarak anormal olan hücreler görüntü üzerinde işaretlenmiştir. Görüntülerden çıkarılan özellikler patoloji uzmanları ile incelenmiş ve bir veri seti oluşturulmuştur. Herlev veri setinde bulunan 917 görüntünün her biri için bu özellikler çıkarılmış ve bir veri setine kaydedilmiştir. Oluşturulan veri setine makine öğrenmesi yöntemlerinden Support Vector Machines (SVM), Naive Bayes, Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K- Nearest Neighbor (KNN) yöntemleri uygulanmıştır ve %83 ve %92 arasında doğruluk değerleri elde edilmiştir.

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