A novel approach for enhanced early breast cancer detection

AYMAZ Ş., Aymaz S.

Computer Methods in Biomechanics and Biomedical Engineering, 2025 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/10255842.2025.2553347
  • Dergi Adı: Computer Methods in Biomechanics and Biomedical Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, Compendex, EMBASE, INSPEC, MEDLINE
  • Anahtar Kelimeler: Breast cancer detection, Grid Search parameter tuning, K-fold cross-validation, long short-term memory (LSTM), Teaching-Learning-Based Optimization (TLBO)
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Breast cancer is a leading cause of women's mortality globally, with early diagnosis crucial for survival. This study addresses diagnostic challenges including imbalanced, noisy datasets and irrelevant features using Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Breast Cancer Database (WBCD) datasets. The proposed approach integrates Custom Adaptive Teaching-Learning-Based Optimization (TLBO) for optimal feature selection and a novel Focal Long Short-Term Memory (Focal LSTM) network to handle imbalanced data effectively. Performance evaluation using accuracy, precision, sensitivity, specificity, F-score, and AUC metrics demonstrates significant improvements. This innovative machine learning approach successfully addresses dataset limitations, contributing robust and accessible diagnostic solutions for healthcare applications.