Kiran, Mustafa ServetSeyfi, GokhanYilmaz, MerveEsme, EnginWang, Xizhao2025-09-102025-09-1020252076-3417https://doi.org/10.3390/app15169053https://hdl.handle.net/20.500.13091/10703Automated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extracts features from multiple and diverse deep learning architectures and classifies them using a Random Weight Network (RWN), whose hyperparameters are optimized for maximum performance. The results show substantial improvements at each stage: while the best standalone deep learning model achieved a test accuracy of 83.55%, applying the RWN to a single feature set increased accuracy to 94.82%. Notably, the proposed feature fusion framework achieved a state-of-the-art test accuracy of 97.44%. These findings demonstrate that a modular approach combining multi-model feature fusion with an efficient classifier is a highly effective strategy for improving the accuracy and generalization capability of automated threat detection systems.eninfo:eu-repo/semantics/openAccessDeep LearningFeature FusionRandom Weight NetworkX-Ray SecurityArticle10.3390/app151690532-s2.0-105014432830