Class-weighted reinforcement learning for skin cancer image classification
Yazarlar (3)
Öğr. Gör. Abubakar Mayanja Karatay Üniversitesi, Türkiye
Prof. Dr. Nurettin Doğan Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Expert Systems with Applications (Q1) | ||
| Dergi ISSN | 0957-4174 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-12-2025 |
| Cilt / Sayı / Sayfa | 293 / 1 / 128426–0 | DOI | 10.1016/j.eswa.2025.128426 |
| Makale Linki | https://doi.org/10.1016/j.eswa.2025.128426 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| As our skin is exposed to ultraviolet rays or dangerous chemicals, aberrant growth of skin cells happens which brings up undesirable conditions such as premature skin aging, transposition in skin texture, and the worst-case scenario skin cancer. In the struggle to combat deadly skin cancer, machine learning can be a useful weapon to help dermatologists make better and clearer decisions while diagnosing patients. Despite promising results with numerous machine learning techniques, this field faces data inadequacy, more so the universally available datasets are subjected to data imbalances. In order to tackle the significant class imbalance present in datasets like the HAM10000 skin cancer dataset, this research introduces a class-weighted reward mechanism within the Deep Q-Learning framework that dynamically allocates higher positive rewards for the accurate classification of rare classes and imposes more … |
| Anahtar Kelimeler |
| Deep Q-learning | Machine learning | Neural networks | Reinforcement learning |
Science Direct
Class-weighted reinforcement learning for skin cancer image classification
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 7 |
| Scopus | 8 |
| Google Scholar | 9 |