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A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features
Scientific Reports · Aralık 2025
YÖKSİS Kayıtları
A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features
Scientific Reports · 2025 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ AYNUR YONAR →
Makale Bilgileri
DergiScientific Reports
Yayın TarihiAralık 2025
Cilt / Sayfa15
Scopus ID2-s2.0-105020283696
Erişim🔓 Açık Erişim
Özet
Accurate automated classification of brain tumors from magnetic resonance imaging (MRI) is essential for early diagnosis and treatment. This study presents a hybrid framework combining Convolutional Neural Network (CNN) deep features, Large Margin Nearest Neighbor (LMNN) metric learning, and swarm-intelligence optimization for robust four-class classification. Five pretrained CNNs—DenseNet201, MobileNetV2, ResNet50, ResNet101, and InceptionV3—were evaluated on a dataset of 7,023 images categorized as glioma, meningioma, pituitary, healthy. Among these, DenseNet201 provided the highest baseline performance with 92.66% accuracy. LMNN improved feature separability, while Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) selected compact subsets. The selected features were classified using k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The DenseNet201–LMNN–GWO–KNN configuration, termed DenseWolf-K, achieved the best performance with 99.64% accuracy, establishing it as the optimal implementation of the framework. Robustness and generalizability were further confirmed using an independent external dataset. Model explainability was ensured through feature-level ranking of GWO-selected features and occlusion sensitivity maps, an Explainable Artifical Intelligence (XAI) method. Overall, the proposed DenseWolf-K framework delivers high accuracy, low false-negative rates, compact representation, and enhanced interpretability, representing a reliable and efficient solution for MRI-based brain tumor classification.
Yazarlar (1)
1
Aynur Yonar
Anahtar Kelimeler
Brain tumor classification
Convolutional neural networks
Feature selection
Metric learning
Swarm intelligence
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey