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Scopus YÖKSİS DOI Eşleşti SJR Q1

Class-weighted reinforcement learning for skin cancer image classification

Expert Systems with Applications · Aralık 2025

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YÖKSİS Kayıtları
Class-weighted reinforcement learning for skin cancer image classification
Expert Systems with Applications · 2025 SCI-Expanded
Prof. Dr. NURETTİN DOĞAN →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Game-theoretic DEA Optimization for Sustainable Agricultural Carbon Trading: Evidence from Türkiye’s Maize Production
2026 ISSN: 0957-4174 SCI-Expanded Q1
Prof. Dr. ZEKİ BAYRAMOĞLU →
Web based medical decision support system application of Coronary Heart Disease diagnosis with Boolean functions minimization method
2011 ISSN: 09574174 SCI
Prof. Dr. FATİH BAŞÇİFTÇİ →
Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
2011 ISSN: 09574174 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
A fuzzy clustering approach for finding similar documents using a novel similarity measure
2007 ISSN: 0957-4174 SCI-Expanded 22 atıf
Doç. Dr. KEMAL TÜTÜNCÜ →
A new approach on search for similar documents with multiple categories using fuzzy clustering
2008 ISSN: 0957-4174 SCI-Expanded
Doç. Dr. KEMAL TÜTÜNCÜ →
Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS
2012 ISSN: 09574174 SCI-Expanded
Prof. Dr. NİMET YAPICI PEHLİVAN →
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
2011 ISSN: 09574174 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
2011 ISSN: 0957-4174 SCI-Expanded 4 atıf
Prof. Dr. MURAT CİNİVİZ →
Fuzzy expert system design for operating room air-condition control systems
2009 ISSN: 0957-4174 SCI-Expanded 29 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Prognosis of prostate cancer by artificial neural networks
2010 ISSN: 0957-4174 SCI-Expanded 30 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Prediction of diesel engine performance using biofuels with artificial neural network
2010 ISSN: 0957-4174 SCI-Expanded 53 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
The effects of fuzzy control of magnetic flux on magnetic filter performance and energy consumption
2010 ISSN: 0957-4174 SCI-Expanded 5 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
2011 ISSN: 0957-4174 SCI-Expanded 24 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Anesthetic gas control with neuro fuzzy system in anesthesia
2010 ISSN: 09574174 SCI-Expanded
Prof. Dr. RÜŞTÜ GÜNTÜRKÜN →
An Adaptive Network Based Fuzzy Inference System ANFIS for the prediction of stock market return The case of the Istanbul Stock Exchange
2010 ISSN: 09574174 SCI-Expanded
Prof. Dr. MELEK ACAR →
Predicting direction of stock price index movement using artificial neural networks and support vector machines The sample of the Istanbul Stock Exchange
2011 ISSN: 09574174 SCI-Expanded
Prof. Dr. MELEK ACAR →
Predicting bank financial failures using neural networks support vector machines and multivariate statistical methods A comparative analysis in the sample of savings deposit insurance fund SDIF transferred banks in Turkey
2009 ISSN: 09574174 SCI-Expanded
Prof. Dr. MELEK ACAR →
The design of ultrasonic therapy device via fuzzy logic
2011 ISSN: 09574174 SCI-Expanded
Öğr. Gör. SEMA YILDIRIM →
Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization
2009 ISSN: 09574174 SCI-Expanded
Prof. Dr. NAZİF AYGÜL →
Design of a hybrid system for the diabetes and heart diseases
2008 ISSN: 0957-4174 SCI-Expanded 85 atıf Q1
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →

Makale Bilgileri

Dergi Expert Systems with Applications
ISSN09574174
Yayın TarihiAralık 2025
Cilt / Sayfa293
DOI10.1016/j.eswa.2025.128426
Scopus ID2-s2.0-105008794903
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 substantial penalties for the incorrect classification of common classes. This strategy encourages the DQN agent to focus on underrepresented categories during the training process, thereby reducing bias towards majority classes. Quantitative assessment metrics such as Accuracy, Precision, F1-score, Specificity, and Sensitivity were used to evaluate the model. The results showed an accuracy of 97.97 %, sensitivity of 97.74 %, precision of 97.81 %, F1-Score of 97.70 %, and specificity of 97.83 % on a non-augmented dataset of HAM10000. Finally, the model performance was compared to that of already existing research work, and it had an upper hand with considerable differences over the existing ones.

Yazarlar (3)

1
Abubakar Mayanja
ORCID: 0000-0003-4576-5771
2
Nurettin Doğan
ORCID: 0000-0002-8267-8469
3
Şakir Taşdemir

Anahtar Kelimeler

Deep Q-learning Machine learning Neural networks Reinforcement learning

Kurumlar

Karatay Üniversitesi
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Dergi sayfasına git

Metrikler

8
Atıf
3
Yazar
4
Anahtar Kelime

Sistemimizdeki Yazarlar

ND
NURETTİN DOĞAN
Prof. Dr.

Hızlı Erişim

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