The effect of informing women referring to health centers on their attitudes about HPV vaccine

Yıldırım, Ediz

doi:10.2399/tahd.11.153

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In 2005, 260,000 women died from this disease in the world, and approximately 95% of these deaths occurred in developing countries. While the expected 5-year survival rate in patients is 66% in developed countries, it is below 50% in developing countries [7]. According to a study in Turkey in 2005, cervical cancer (5.31%) ranks 10th among the 10 most common diseases in women [8].…”

Section: Introductionmentioning

confidence: 99%

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Serviks Kanser Hücrelerinin Ön-Eğitimli Ağ Temelli Otomatik Sınıflandırılması İçin Bir Ara Yüz Tasarımı ve Geliştirilmesi

BİNGÖL¹,

Uzun²,

ÖZKURT³

2022

European Journal of Science and Technology

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Cervical cancer is the second most common cancer in women after breast cancer, causing the death of one woman every two minutes in the world. The most important risk factor originating from the cervix is infection with the papilloma virus (HPV). Cervical cancer screening programs are extremely important to reduce the incidence and death rates of this cancer. The primary goal of screening for cervical cancer is the accurate detection and timely treatment of intraepithelial precursor lesions of the cervix, in order to prevent cervical cancer. With the PAP smear test, cells in the cancerous stage are detected in the endocervical canal, and cancer development can be prevented before the cells turn into cancer with cancer treatment at this stage. The PAP test, which is used in early diagnosis, is an easyto-apply, low-cost, harmless, high-sensitivity test that also reduces the burden of treatment. Recent developments in the field of artificial intelligence have achieved serious success in the diagnosis of cervical cancer. In this study, a transfer learning-based cervical cancer detection method and an application developed to easily perform these procedures are presented. Cancerous and non-cancerous cervical cells were classified using pre-trained networks. Five popular pre-trained networks, namely Xception, VGG-16, DenseNet, InceptionV3, and InceptionResNetV2, were used for the problem and the obtained performance results were compared. In addition, an application has been developed so that experts working in this field can easily make such classifications. With this application, users can create their own models by conducting a new training, use the model created in this study, and quickly test which class the newly obtained images belong to. As a result of the study, DenseNet network obtained the highest accuracy with 94.72% accuracy. Experimental results show that the proposed approach can provide an inexpensive and rapid decision support system for cervical cancer detection that anyone can apply.

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Section: Introductionmentioning

confidence: 99%

“…Risk factors that cause cancer include exposure to papillomavirus (HPV), smoking, and immune system dysfunction [6]. Early diagnosis is key to preventing death [7]. Cervical cancer screening in Turkey is performed by HPV DNA or pap-smear test applied every 5 years to women aged 30-65.…”

Section: Introductionmentioning

confidence: 99%