Machine Learning Approaches in Cervical Cancer Research: A Comprehensive Literature Review
Yazarlar (2)
Murathan Yüksel Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/AICCONF64766.2025.11064277 | ||
| Kongre Adı | 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF) | ||
| Kongre Tarihi | 13-06-2025 / 14-06-2025 | ||
| Basıldığı Ülke | Çek Cumhuriyeti | Basıldığı Şehir | Prague |
| Bildiri Linki | https://doi.org/10.1109/aicconf64766.2025.11064277 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Cervical cancer (CrC) continues to pose a significant global threat to women’s health and accounts for a considerable share of the worldwide cancer burden. Early diagnosis and effective management strategies play a critical role in reducing the morbidity and mortality rates associated with the disease. This review aims to comprehensively evaluate the epidemiology, etiology, traditional screening and diagnostic methods, and the current and potential applications of machine learning (ML) and artificial intelligence (AI) algorithms in cervical cancer. The reviewed studies cover a broad spectrum, ranging from the role of Human Papillomavirus (HPV) infection in disease progression to cancer cell classification, prediction of treatment response, and individual risk assessment. This review highlights the transformative potential of ML and AI techniques in the fight against cervical cancer, demonstrating how advanced … |
| Anahtar Kelimeler |
| artificial intelligence | cervical cancer | clinical decision support systems | colposcopy | data balancing | early diagnosis | ensemble learning | epidemiology | explainable artificial intelligence (XAI) | feature selection | human papillomavirus (HPV) | machine learning | medical image processing | Pap smear | risk prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 2 |
| Google Scholar | 2 |